# LNA-Net: Enhancing detection and classification of benign and malignant pulmonary nodules in CT scans

Zhifan Song, Bin Song, Abd Al Rahman M. Abu Ebayyeh

► To cite this version:

Zhifan Song, Bin Song, Abd Al Rahman M. Abu Ebayyeh. LNA-Net: Enhancing detection and classification of benign and malignant pulmonary nodules in CT scans. Biomedical Signal Processing and Control, 2026, 117, pp.109505. 10.1016/j.bspc.2026.109505. hal-05465750

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Submitted on 19 Jan 2026

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# LNA-Net: Enhancing detection and classification of benign and malignant pulmonary nodules in CT scans

Zhifan Song<sup>a,c</sup>, Bin Song<sup>b</sup>, Abd Al Rahman M. Abu Ebayyeh<sup>c,d</sup>,*

## ARTICLE INFO

### Keywords:

- Deep learning

- Pulmonary nodule

- Object detection

- Medical imaging

## ABSTRACT

Lung cancer remains a leading cause of cancer-related mortality worldwide, highlighting the critical need for accurate and efficient detection methods. CT scans involve multiple layers of imaging, requiring meticulous examination to identify lung nodules. Traditional manual methods and many AI-based detection approaches are often slow and computationally intensive. In this paper, we present LNA-Net, a novel lightweight deep learning framework designed for real-time detection and streamlined classification of benign and malignant pulmonary nodules in CT scans. We introduce the novel Adaptive Kernel Attention Module (AKAM), a plug-and-play attention block that enhances the extraction of features for varying-size and small nodules, along with a customized C2fCNB block to improve feature fusion in the neck of the network. By integrating nodule detection and malignancy classification into a unified pipeline, LNA-Net eliminates the need for an additional classifier. Evaluated on the LUNA16 dataset, our model outperformed state-of-the-art (SOTA) object detection frameworks, achieving 82.5% mAP@50 for nodule detection, representing a 3.4% improvement over YOLOv10n, along with 93.4% recall and 93.3% F1-score in classifying nodules, a 9.4% and 5.4% enhancement compared to the YOLOv10 backbone respectively. LNA-Net's high accuracy and real-time efficiency position it as a valuable tool for assisting radiologists in the timely diagnosis of lung cancer.

## 1. Introduction

Lung cancer, a type of malignant tumor, remains one of the leading causes of cancer-related mortality worldwide, with a high mortality rate that underscores the urgent need for improved diagnostic and therapeutic strategies [1]. Early and accurate diagnosis is pivotal to reducing lung cancer mortality, and computed tomography (CT) scans have proven instrumental in achieving this goal, with studies demonstrating a 20% reduction in mortality through early detection [2,3]. Integrating deep learning techniques with CT imaging has emerged as a promising solution, offering substantial potential for enhancing diagnostic accuracy and efficiency [4].

CT scans typically involve multiple layers of imaging in the transverse, coronal, and sagittal planes, requiring thorough examination to identify lung nodules. Traditional methods impose a considerable burden on radiologists, who may need to examine around 40,000 scans daily [5], relying on manual inspection. This immense workload not only contributes to fatigue but also undermines diagnostic accuracy. Computer-aided diagnosis (CAD) systems have been introduced to address these challenges by enhancing detection capabilities and reducing

human effort. [6]. Nevertheless, conventional image processing and machine learning methods, such as morphological analysis [7] and clustering [8], suffer from limited generalizability and accuracy. Similarly, segmentation-based methods often rely on manual feature extraction, resulting in suboptimal performance [9].

Deep learning has emerged as a more prominent solution and has demonstrated significant advancements in target detection. While two-stage detectors like Faster-RCNN [10] achieve high accuracy, they suffer from slower inference speeds. Transformer-based approaches [11], known for their accuracy in medical imaging [12,13], introduce computational complexity and may exhibit poor generalization when trained on small datasets [14], due to the absence of inherent inductive biases found in other models, such as convolutional neural networks (CNNs).

In contrast, one-stage object detectors, particularly the YOLO (You Only Look Once) [15] family, have demonstrated a compelling balance of real-time performance and high accuracy across various domains [16–18]. YOLO-based frameworks have also been applied to lung nodule detection [9,19–21], primarily focusing on improving mean average precision (mAP) and recall metrics.

<footnote><sup>a</sup> LIP6 Laboratory, Sorbonne University, CNRS UMR 7606, Paris, 75252 CEDEX 05, France</footnote>

<footnote><sup>b</sup> Department of Thoracic Surgery, Huzhou Gospel Hospital, Huzhou University, Huzhou, 313000, Zhejiang, China</footnote>

<footnote><sup>c</sup> Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, United Kingdom</footnote>

<footnote>* Corresponding author.
E-mail addresses: zhifan song@lip6.fr (Z. Song), hzsong2022@163.com (B. Song), a.abu-ebayyeh@imperial.ac.uk (A.M. Abu Ebayyeh).</footnote>
Despite these advancements, existing YOLO-based approaches often overlook critical considerations. Many existing works did not address the real-time inference speed to tackle the problem of a large quantity of CT scans per day, and focus exclusively on either nodule detection or malignancy classification without addressing the two tasks within a unified framework. Furthermore, the reliance on earlier YOLO versions that use non-maximum suppression (NMS) for post-processing introduces computational overhead, hindering real-time applicability. To the best of our knowledge, there has been limited exploration of using the YOLOv10 architecture [22], which eliminates NMS through dual label assignment, as a baseline for lung nodule applications. While YOLOv10 offers significant advantages for real-time performance, challenges remain in accurately detecting small nodules with similar patterns.

To address these challenges, we propose Lung Nodule Analysis Network (LNA-Net), a real-time framework designed for integrated and streamlined lung nodule detection and malignancy classification. The key contributions of this work include:

*   Adaptive Kernel Attention Module (AKAM): A plug-and-play attention block designed to enhance multi-scale feature extraction, particularly for small target detection, with proven effects on nodule detection.

*   C2fCNB block: An improved bottleneck module for the C2f structure, enhancing feature fusion for robust neck performance.

*   A streamlined backbone design that integrates detection and malignancy classification, eliminating the need for a separate classifier while enhancing accuracy and recall.

*   Performance Improvement: On the LUNA16 dataset [23], our method achieves 82.5% mAP@50 for nodule detection (3.4% higher than baseline), and 93.4% recall and 93.3% F1-score for malignancy classification, with 9.4%, and 5.4% gains, while maintaining real-time speed across diverse hardware.

## 2. Related work

### 2.1. Attention mechanisms and multi-scale networks

The attention mechanism, initially introduced in natural language processing (NLP) for machine translation [24], enables the modeling of long-range dependencies by selectively focusing on relevant parts of the input. Over time, it has evolved into variants such as self-attention [11], which serves as the backbone of the Transformer, significantly improving computational efficiency and accuracy across diverse applications. These mechanisms have also achieved widespread adoption in computer vision (CV), particularly through convolutional attention, with channel, spatial, temporal, and branch channel dimensions [25].

For CV applications, the Squeeze-and-Excitation (SE) attention [26] recalibrates channel-wise features by modeling global interdependencies. However, SE's focus solely on channel attention limits its ability to capture spatially complex structures, such as small lung nodules. The Convolutional Block Attention Module (CBAM) [27] extends SE by incorporating both channel and spatial attention. Nevertheless, CBAM's sequential application of these attentions may introduce redundancy, limiting its capacity to efficiently fuse multi-scale features critical for detecting nodules of diverse sizes. Selective Kernel (SK) attention [28] addresses this limitation by dynamically selecting receptive fields of varying sizes, and adapting to scale variations. Despite this improvement, SK remains computationally intensive and may not fully exploit spatial information in medical imaging tasks. Similarly, the Global Attention Mechanism (GAM) [29] leverages global pooling to enhance channel and spatial attention, effectively capturing global context. However, GAM often struggles with local feature representation, particularly necessary for identifying small or subtle nodules.

To overcome these limitations, multi-scale networks and attention mechanisms have gained prominence, particularly in tasks requiring

scale diversity and intricate textural details, such as lung nodule detection. GoogleNet [30] pioneered multi-scale networks with the inception module, demonstrating its effectiveness in image classification. Subsequently, multi-scale networks have been applied to various tasks, including semantic segmentation [31] and small object detection [32, 33], enhancing feature extraction across scales.

Recent advancements like Multi-Scale Convolutional Attention (MSCA) [34] integrate local and global features efficiently by combining depth-wise strip convolutions, improving the network's ability to detect objects of varying sizes while reducing computational overhead. Similarly, Poly Kernel Inception (PKI) Networks [35] enhance multi-scale feature extraction by employing parallel kernel combinations, reminiscent of inception structures.

In the context of lung nodule detection, nodules present diverse scales, shapes, and contrast levels, making multi-scale attention mechanisms particularly effective. By capturing and fusing features across scales, these methods address the inherent challenges of earlier attention-based approaches, offering superior performance in detecting and classifying lung nodules.

### 2.2. Deep learning-based lung nodule detection and classification

In recent years, numerous solutions have been developed to enhance lung nodule detection capabilities [36]. While traditional methods exhibit limited performance, data-driven deep learning techniques have demonstrated substantially higher accuracy and reliability [37,38].

Multi-scale attention mechanisms, discussed previously, have proven particularly effective for lung nodule detection with deep learning models [5,19,20,39,40]. Several studies have also focused on adapting and improving the YOLO models for lung nodule detection. Ji et al. [21] integrated CBAM, enhanced the feature pyramid, and introduced a contextual transformer, achieving a 72% mAP. However, this method results in a computationally intensive model with 78 M parameters. Similarly, STBi-YOLO [9] improved YOLOv5 by incorporating stochastic-pooling-based SPPNet, Bidirectional Feature Pyramid Network (BiFPN) [41], and an improved loss function, achieving 77.1% mAP but with a higher parameter count than YOLOv5s, downgrading processing efficiency.

Other studies have leveraged multi-scale feature extraction to improve detection performance. For example, YOLOv7 enhancements [19] include a multi-scale receptive field module and an efficient ODConv [42], while YOLOv6 modifications [20] integrate multi-scale dual-branch attention and a cross-layer aggregation module to augment BiFPN [41], preserving multi-level fine-grained information.

Despite these advances, most approaches prioritize detection accuracy without addressing computational efficiency for large-scale batch processing, which is a critical consideration in clinical settings. Additionally, these works focus exclusively on nodule detection, neglecting the equally important task of malignancy classification. Accurate classification of benign and malignant nodules is essential for early lung cancer diagnosis [43].

Deep learning's potential for malignancy classification has been demonstrated in various studies [43,44]. The LUNA16 dataset [23], a widely used benchmark for lung nodule detection, includes an extracted Lung Nodule Malignancy dataset available on Kaggle. This dataset ensures consistency in evaluating systems that integrate real-time lung nodule detection and classification, addressing both tasks within a unified framework for streamlined clinical application.

## 3. Methodology

### 3.1. Proposed framework

We propose LNA-Net, a framework for streamlining lung nodule detection and malignancy classification into a unified model, with the complete architecture for nodule detection and a recursively used
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** The proposed LNA-Net framework. Feature map dimensions are annotated on the diagram for reproducibility. The detailed structure of the proposed modules is illustrated on the left (C2fCNB fusion block) and right (SPPF and AKAM) panels.</figcaption></figure>

backbone with weights updating for malignancy classification. The model aims at enhancing detection speed while ensuring and improving accuracy compared to major object detection models used for medical object detection tasks.

YOLO-based architectures have gain substantial adoption in medical object detection tasks, such as lesion [45], skin cancer [46], colon polyp [47,48], brain tumor [49], and cardiac abnormality detection [50], due to their real-time efficiency and strong accuracy. YOLO's ability to process multiple targets per frame with low latency makes it a practical baseline for medical object detection, where preprocessing speed and diagnostic throughput are critical [51].

In our work, we also adopt YOLO as the architectural foundation and YOLOv10's efficient post-processing free label assignment and fast inference, which are particularly beneficial for processing high volumes of CT scans. Consistent with YOLO's design decisions, LNA-Net comprises a backbone for feature extraction, a neck for feature fusion, and a head for final detection [15,22], which is also the same as other studies incorporating YOLO as a baseline for lung nodule detection [19–21]. The high-level architecture is shown in Fig. 1.

While our architecture design follows the well-established YOLO paradigm, the general object detection model design has key limitations in small target detection. Although adding an additional detection head could enhance small target detection [52–54], this would lead to multiple extra layers in the neck and increase the computational complexity of the model [55]. Moreover, prior works often only aim at enhance YOLO with higher accuracy, with new modules at the cost of increased parameters and inference time. In contrast, we aim to retain the number of layers while enhancing the model's ability to detect multi-scale targets, enhancing small target and overall target detection performance at the same time with AKAM and C2fCNB as plug-and-play replacement blocks that target key limitations of existing YOLO-based medical detectors. Our design speeds up inference while enhancing detection accuracy, precision, and recall, which will be supported by ablation studies presented in Sections 4 and 5. These lightweight modules can be used to generalize to other medical detection tasks or be integrated into existing object detectors to enhance performance on small-target, domain-specific data.

LNA-Net's layout follows the conventional structure of a one-stage detector, comprising a backbone, neck, and head, similar to the YOLO family of models, which are widely adopted for real-time object detection tasks [56], but with targeted enhancements tailored for the lung nodule detection task. These include domain-specific adjustments

in feature extraction and fusion to enhance sensitivity to small, low-contrast nodules, which are often missed by generic detectors. A detailed explanation of these architectural components and their distinctions from standard YOLO variants is provided in Sections 3.2, 3.3, 4.

The backbone downsamples the input image of size $F = 640 \times 640$, across five stages, producing feature maps $P_i$ with resolutions ranging from 160 ($P_1$) and 20 ($P_5$). The resolution at each stage is given by $P_i = \frac{F}{2^i}$, where $i \in \{1,2,3,4,5\}$. This hierarchical representation is well-suited for detecting pulmonary nodules, which can appear at varying sizes and locations. We use CBS (Conv2d-BatchNorm-SiLU) blocks for spatial downsampling, followed by C2f modules, which improve the feature expressiveness through lightweight bottlenecks and partial gradient flow. These modules have proven effective in prior work [57], and are retained here to efficiently refine multi-scale features.

C2f, which stands for CSP (Cross Stage Partial) Bottleneck with 2 convolutions (faster), originated in YOLOv8 [57], which uses several bottlenecks consisting of two CBS blocks to refine the feature maps and to form layers of feature pyramid. The architecture of C2f blocks is depicted on the bottom left of Fig. 1. It uses the CBS block to expand feature space and uses bottlenecks for enhancing feature processing at various scales. According to the author of YOLOv8, C2f modules are designed to enhance feature extraction by capturing more complex patterns in the data [57]. CBS and C2f modules are used in sequence in the backbone to refine the feature maps and the post-concatenation C2f modules in the neck help in further enhancing the feature representation before detection.

To further increase receptive field coverage without losing resolution, we include a Spatial Pyramid Pooling-Fast (SPPF) block [58]. This helps capture nodules that may be faint, low-contrast, or attached to vessel walls, where contextual information is crucial.

The neck combines elements of Feature Pyramid Networks (FPN) [59] and Path Aggregation Networks (PAN) [60], performing top-down and bottom-up feature fusion across scales. This design is essential for lung CT analysis, where nodules can vary from a few pixels to larger masses. Importantly, we introduce lightweight C2fCNB modules post-concatenation, improving the fused feature representation while minimizing additional computational overhead.

The head structure in LNA-Net departs from earlier YOLO versions (e.g., YOLOv5/YOLOv8), and follows a design closer to YOLOv10. We decouple classification and regression branches to prevent task interference and improve localization accuracy, especially for small nodules. An optimized loss function to improve bounding box regression for
<figure><img src="image_4.png" /><figcaption>Fig. 2. Feature processing visualization across LNA-Net. Class activation heatmaps are extracted at each stage (P1–P5) of the backbone and neck. The figure shows feature map dimensions, multi-scale fusion paths, and how attention progressively focuses around nodules through successive stages.</figcaption></figure>

tackling artifacts such as motion blur, beam hardening, and noise is also introduced, and will be discussed in Section 4. Furthermore, our design omits the P2-level detection head (common in other works targeting small objects), relying instead on optimized feature fusion and the AKAM module to enhance attention to fine-grained patterns without deepening the model or slowing inference. This makes LNA-Net better suited for real-time deployment in clinical screening scenarios.

The visualization of our feature processing pipeline, including feature map dimensions and normalized Class Activation Maps (CAMs) using EigenCAM, is shown in Fig. 2. It illustrates how multi-scale features are extracted through five backbone stages (P1–P5) and subsequently fused through top-down and bottom-up pathways with lateral concatenations across matching stages. The figure highlights real activation heatmaps at each stage of LNA-Net, showing how spatial attention around nodules evolves through the feature hierarchy, guided by the AKAM and C2fCNB modules.

The complete framework performs the target detection task like conventional object detectors but incorporates a tailored and enhanced design specifically for lung nodule detection. Upon detecting nodule coordinates, the nodules are treated as separate image pixels and re-injected into the backbone with updated pre-trained weights through a switching mechanism (CIs Switch), as depicted in Fig. 3. This approach eliminates the need for an additional classifier, whether convolutional-based [61–65] or transformer-based [11,66–70] mainstream models, thereby streamlining the process, reducing model complexity, significantly reduce memory impact and improving inference speed. The pipeline, illustrated in Fig. 3, is highly efficient and versatile, supporting deployment across various runtime environments, including TensorRT, ONNX, OpenVINO, and CoreML. This ensures compatibility with a wide range of hardware platforms (GPU/CPU/NPU) from common manufacturers such as NVIDIA, Intel, and ARM, etc.

## 3.2. Adaptive Kernel Attention Module

To improve LNA-Net’s sensitivity to small, low-contrast nodules in CT images, we design a lightweight convolutional attention module, with its detailed architecture shown in Fig. 4, aimed at enhancing multi-scale feature extraction from small lung nodules of varying sizes. Lung nodules can have various sizes, from small nodules of 3 mm to more than 8 mm in the selected dataset, as highlighted in the dataset distribution illustration in Fig. 6. Therefore, we propose a convolutional attention module that consists of a Multi-Scale Kernel with Adaptive Dilation (MSKAD) stage and a Spatial Attention (SA) stage. We use depthwise convolution (DWConv) with an adaptive dilation factor to effectively capture multi-scale features.

Firstly, we have more efficient parameterization leading to reduced computational complexity. A standard convolution (Conv) has a complexity of $C \times K^2 \times H \times W$, where C is the number of channels, K is the kernel size, and H and W represent height and width. A DWConv has its complexity reduced by C to $K^2 \times H \times W$ as it processes each channel independently. Here at the end of the backbone of 256 channels, the parameter reduction is nearly 256 times.

Secondly, We choose to add adaptive dilation to DWConv which not only enhances the computational efficiency with sparse connections but expands the receptive field for better contextual awareness, as the texture in CT scans has various similar patterns, and small targets in images are often contextually dependent on surrounding features hence can benefit from a receptive field that covers a wider area. Increasing the dilation rate in depthwise convolutions adaptively allows the network to perceive patterns at multiple spatial resolutions without explicitly adding more layers or parameters, crucial for small object detection where fine-grained details are critical.

We adopt the SiLU activation function, following recent YOLO-based architectures (e.g., YOLOv8, YOLOv10). SiLU’s smooth and non-monotonic properties improve gradient flow and allow for more expressive modeling of subtle patterns. In the context of lung nodule detection, this helps the network capture faint, low-contrast nodules and complex texture variations that are often missed by conventional activations such as ReLU. Its effectiveness in deep networks also supports better multi-scale fusion across stages, and ensures both architectural compatibility and performance benefits as demonstrated in prior studies [22,57,71,72].

We first define our two types of convolution operators. Let $X_{l-1} \in \mathbb{R}^{C \times H \times W}$ be an input tensor, where C is the number of channels, and H, W are spatial dimensions. Let $W \in \mathbb{R}^{C \times C}$ be the weights tensor, PWConv is applied independently at each spatial location $(h, w)$ for channel information mixing :

$$X_{l',h,w} = \sum_{c=1}^{C} W_{c',c} \cdot X_{l-1,c,h,w}$$

In depthwise convolution, each input channel is convolved separately using its own spatial kernel. Let $X_{l-1} \in \mathbb{R}^{C \times H \times W}$, and let $K_c \in \mathbb{R}^{k \times k}$ be the kernel for channel c:

$$X_{l,c,h,w} = \sum_{i,j} K_c(i, j) \cdot X_{l-1,c} (h+d \cdot i, w+d \cdot j)$$

where d is the dilation factor. The kernel samples input at intervals, and DWConv leads to significant parameter reduction compared to standard convolution.

In the design of the AKAM module, the first PWConv linearly combines the input channels. This operation prepares the feature maps
<figure><img src="image_5.png" /><figcaption>Fig. 3. The lung nodule detection and classification pipeline with four supported hardware acceleration modes. The blue shaded part represents the reconfigurable backbone and the red shaded area illustrates the neck. The model first performs nodule detection on the pre-processed CT scan, then re-injects the detected nodule to the backbone with weights updating and performs the malignancy classification task.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 4. The proposed Adaptive Kernel Attention Module (AKAM). PWConv: Pointwise Convolution; DWConv: Depthwise Convolution; d: dilation. MSKAD: Multi-Scale Kernel with Adaptive Dilation; SA: Spatial Attention.</figcaption></figure>

for enhanced multi-scale aggregation and attention by capturing inter-channel correlations, where BS denotes BatchNorm+SiLU:

$$I_{l-1} = \text{PWConv}_{BS}(X_{l-1}) \qquad (1)$$

Then, the adaptive dilated depthwise convolutions (ADDConv) capture features at various scales in parallel, and the outputs are fused by summation and projected via PWConv that aggregates information from various receptive fields along with the identity input $I_{l-1}$, enhancing the feature representation:

$$M_{l-1} = \text{PWConv}_{BS} \left( I_{l-1} + \sum_{i=0}^{2} \text{DWConv}_{BS}^{(i)} (I_{l-1}; d = i + 1) \right) \qquad (2)$$

A PWConv followed by a Sigmoid (Sig) activation generates an attention map $A_{l-1}$ that models channel-wise relations. As commonly applied, the Sigmoid activation bounds the attention values, functioning as a gating mechanism:

$$A_{l-1} = \text{PWConv}_{Sig}(M_{l-1}) \qquad (3)$$

Finally, the output is obtained by element-wise multiplication of the multi-scale features $M_{l-1}$ with the attention map $A_{l-1}$ to recalibrate the fused feature map:

$$X_l = M_{l-1} \otimes A_{l-1} \qquad (4)$$

This gating operation emphasizes informative features across multiple receptive fields while suppressing irrelevant signals.
<figure><img src="image_7.png" /><figcaption>Fig. 5. The C2f bottleneck comparison. (a) C2f (YOLOv8); (b) C2fCIB (YOLOv10); (c) C2fCNB (LNA-Net).</figcaption></figure>

Compared to some common convolutional attention mechanisms, SE (Squeeze-and-Excitation) [26] performs global average pooling to produce a channel descriptor, followed by two fully connected (FC) layers and a Sigmoid gate. While effective for channel recalibration, SE entirely discards spatial context:

$$s_c = \frac{1}{H \cdot W} \sum_{h=1}^{H} \sum_{w=1}^{W} X_{c,h,w}, \quad \hat{s} = \sigma(W_2 \delta(W_1 s))$$

CBAM [27] adds a spatial attention stage after SE, using max and average pooling followed by a 7 × 7 convolution. However, CBAM applies attention sequentially and is not multi-scale, which may suppress fine features in small nodules:

$$\mathbf{M}_{\text{spatial}} = \sigma(\text{Conv}_{7 \times 7}([\text{AvgPool}(X); \text{MaxPool}(X)]))$$

By contrast, AKAM performs attention over multiple adaptive receptive fields and explicitly incorporates spatial recalibration, enabling superior performance in detecting small, low-contrast nodules with minimal computational overhead. Ablation studies (Table 6) will confirm that AKAM achieves the highest mAP, precision and recall among all tested attention modules, while preserving YOLO network's efficiency.

### 3.3. Enhanced C2f module for improved feature fusion

While AKAM enhances feature extraction of the network for multi-scale targets, we introduce the C2fCNB module to also improve feature fusion and further enhance real-time performance. C2fCNB integrates a ConvNeXt-style [65] bottleneck in to the C2f blocks, replacing original C2f blocks in YOLOv8 and C2fCIB blocks in YOLOv10. The motivation behind the module is that lung nodules are often small and subtle, making feature normalization and fusion crucial for reliable detection (see Fig. 5).

Let $\mathbf{X}_{l-1} \in \mathbb{R}^{C \times H \times W}$ be the input tensor. The forward computation of CNB proceeds as follows:

$$\mathbf{Z}_1 = \text{LayerNorm}(\text{DWConv}_{7 \times 7}(\mathbf{X}_{l-1}))$$

$$\mathbf{Z}_2 = \text{PWConv}_{C \times 4 \times GELU}(\mathbf{Z}_1)$$

$$\mathbf{Z}_3 = \text{GRN}(\mathbf{Z}_2)$$

$$\mathbf{X}_l = \mathbf{X}_{l-1} + \text{PWConv}_{4C \times C}((\mathbf{Z}_3))$$

Here, ⊙ denotes element-wise multiplication, and $γ, β \in \mathbb{R}^C$ are learnable channel-wise parameters.

Lung nodules are often small, subtle abnormalities that can be difficult to distinguish from surrounding lung tissue. During training,

the model may struggle to learn consistent patterns if features are poorly normalized, leading to instability in gradients and learning.

Layer normalization (LayerNorm) [73] is present in the bottleneck to stabilize feature distributions. Unlike BatchNorm, which normalizes across the batch dimension and may suppress rare small features, LayerNorm operates independently on each spatial location and across channels:

$$\text{LayerNorm}(\mathbf{X}) = \frac{\mathbf{X} - \mu}{\sqrt{\sigma^2 + \epsilon}},$$

$$\text{where } \mu = \frac{1}{C} \sum_{c} X_{c, \cdot, \cdot}, \quad \sigma^2 = \frac{1}{C} \sum_{c} (X_{c, \cdot, \cdot} - \mu)^2$$

This preserves subtle variations and stabilizes gradients, which is beneficial in detecting fine-grained medical features such as small nodules.

Global Response Normalization (GRN) [74] layer is also there to prevent feature collapse and enhance global context modeling. This can be crucial for lung nodule detection, where nodules are typically small and may be obscured by surrounding lung tissue. GRN ensures that features are balanced and context-aware, potentially improving sensitivity to small, nuanced regions within the image.

For a feature map $\mathbf{Z} \in \mathbb{R}^{C \times H \times W}$, GRN is computed as:

$$\mathbf{g}_c = \|\mathbf{Z}_{c, \cdot, \cdot}\|_2, \quad \bar{g} = \frac{1}{C} \sum_{c=1}^{C} \mathbf{g}_c$$

$$\text{GRN}(\mathbf{Z}) = \gamma \cdot (\mathbf{Z} \odot \frac{\mathbf{g}}{\bar{\mathbf{g}} + \epsilon}) + \beta + \mathbf{Z}$$

where $γ, β \in \mathbb{R}^C$ are learnable scaling and bias parameters, and $\mathbf{g} \in \mathbb{R}^{C \times 1 \times 1}$ is broadcast spatially.

This operation normalizes the response energy of each channel and helps balance contributions across spatial locations, which is important when detecting small targets in images potentially obscured by surrounding anatomy.

Furthermore, DWConv is used instead of standard convolution to reduce the computational load and make the model more lightweight for faster inference speed. The lightweight nature allows for scalability across different hardware platforms, enabling its use in diverse clinical environments, from centralized diagnostic centers to decentralized settings with portable devices.

In a clinical setting, even small performance gains in lung nodule detection could translate to more accurate and earlier diagnoses of lung cancer, thereby improving patient outcomes. By using C2fCNB
<figure><img src="image_8.png" /><figcaption>Fig. 6. Normalized lung nodule size distribution in the dataset. Nodules vary widely in size, ranging from 3 mm to over 8 mm in diameter. Approximately 20% of nodules fall within the 3–5 mm range, which poses greater detection challenges.</figcaption></figure>

blocks with enhanced global context and fine-grained feature extraction and the AKAM block for better feature extraction, LNA-Net offers a promising solution that balances accuracy with computational efficiency.

## 4. Loss function and robustness to CT artifacts

Loss function is essential to train the network to optimize the weights. For object detection tasks, the loss function comprises classification loss (cross-entropy), bounding box regression loss (IoU-based), and Distribution Focal Loss (DFL) [75] for enhancing bounding box quality by modeling coordinates as distributions.

$$ \text{DFL}(S_i, S_{i+1}) = -((y_{i+1} - y_i) \log(S_i) + (y - y_i) \log(S_{i+1})) \quad (5) $$

where $S(\cdot)$ gives the output of softmax layer, $S_i = P(y_i)$, $y$ is the label and $y_i, y_{i+1}$ are the nearest two to $y$. DFL increases the probability of them and drives the network to approach $y$.

The Generalized IoU (GIoU) loss [76] is defined as follows:

$$ L_{GIoU} = 1 - \frac{|B \cap B^{GT}|}{|B \cup B^{GT}|} + \frac{|C - B \cup B^{GT}|}{|C|} \quad (6) $$

where $C$ is the smallest box covering the prediction and ground truth boundary boxes to alleviate the IoU loss problem where the gradient will be zero in the case of non-overlapping situations.

Nevertheless, GIoU loss has slow convergence for skewed bounding boxes and still cannot ensure accuracy [77]. YOLOv5, YOLOv8 and YOLOv10 all use Complete IoU (CIoU [77]) loss for bounding box regression. In the case of CIoU loss, the penalty term is defined as:

$$ R_{CIoU} = \frac{\rho^2(\mathbf{b}, \mathbf{b}^{GT})}{c^2} + \alpha v \quad (7) $$

where $\mathbf{b}$ and $\mathbf{b}^{GT}$ correspond to the central coordinates of the predicted and ground truth bounding boxes $B$ and $B^{GT}$, $\rho(\cdot)$ indicates the Euclidean distance and $c$ denotes the diagonal length of the smallest box encompassing the predicted and ground truth bounding boxes.

$v$ indicates the aspect ratio consistency level:

$$ v = \frac{4}{\pi^2} \left( \arctan \frac{w}{h^{GT}} - \arctan \frac{w}{h} \right)^2 \quad (8) $$

where $\frac{w}{h}$ and $\frac{w^{GT}}{h^{GT}}$ denote the prediction and ground truth aspect ratio.

$\alpha$ is a trade-off parameter that favors the overlapping area factor more, particularly for non-overlapping cases:

$$ \alpha = \frac{v}{(1 - IoU) + v} \quad (9) $$

While YOLOv10 uses CIoU as default, it can struggle to generalize in the presence of varying object sizes, texture inconsistencies, and noise—common in pulmonary nodule detection. To address this, we

replace CIoU with Wise-IoU (WIoU) [78], which has demonstrated improved robustness across multiple object detection tasks. WIoU mitigates the influence of large, harmful gradients from low-quality predictions, thereby stabilizing training. It has been successfully applied in domains such as drone imagery to enhance small target detection [79], noisy environments to improve bounding box quality [54], and occlusion-heavy scenarios [80].

We propose to employ WIoUv2 to train LNA-Net:

$$ L_{WIoU} = R_{WIoU} L_{IoU} \quad (10) $$

$$ R_{WIoU} = \exp \left( \frac{(x - x_{gt})^2 + (y - y_{gt})^2}{(W^2 + H^2)^*} \right) \quad (11) $$

$$ L_{WIoUv2} = r L_{WIoU} \quad (12) $$

$$ r = \left( \frac{L_{IoU}^*}{L_{IoU}} \right)^\gamma \quad (13) $$

In these equations, $L_{IoU} = 1 - IoU$, $(x, y)$ represents the coordinates of the ground truth bounding box, $(x_{gt}, y_{gt})$ are the predicted coordinate, and $W$ and $H$ denote the width and height of the minimal enclosing box between the two, $r$ is the gradient gain, $L_{IoU}^*$ is the exponential running average with momentum, and $\gamma = 0.5$.

CT scans frequently exhibit artifacts such as motion blur, beam hardening, and imaging noise. WIoUv2 uses a monotonic focusing coefficient based on focal loss to increase emphasis on more difficult samples while down-weighting easier ones [81], and reduces harmful gradients from low-quality samples [79], encouraging the model to concentrate on hard and ambiguous cases. The gradient increment $L_{IoU}^*$ is normalized using a moving average to maintain high and stable gradient gains, which further supports better detection performance for objects of varying size and quality.

## 5. Results and discussion

### 5.1. Dataset

Two publicly available datasets are used in this paper. We utilize the LUNA16 dataset [23], which is a widely used benchmark in lung nodule detection research. For the nodule detection set, 888 CT Scans and 1186 nodules with various diameters annotated by experts. The dataset split ratio is 80:10:10.

The distribution and nodule sizes of the LUNA16 dataset are illustrated in Fig. 6. The dataset is also preprocessed by converting images to binary masks, isolating and refining lung regions, and applying various morphological operations to extract clean lung fields.
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Step-by-step visualization of the preprocessing pipeline. (a) Original frame, (b) Thresholding/binarization, (c) Blob filtering, (d) Lung segmentation, (e) Morphological mask refinement, (f) Convex hull computation for left and right lungs, (g) Lung region reconstruction, (h) Preprocessed frame.</figcaption></figure>

A derived lung nodule malignancy dataset based on LUNA16 is also available and is utilized in this paper for the streamlined malignancy classification after nodule detection. The dataset contains 6691 scans of which 4165 are benign and 2526 are malignant. The split ratio for the classification set is 60:40.

## 5.2. Preprocessing

The preprocessing of the LUNA16 dataset follows standard practices commonly adopted in lung nodule detection tasks, as seen in prior works [19,20,82]. These steps are essential to isolate lung regions and reduce false positives before applying AI-based models. First, we apply thresholding to binarize the image and remove redundant intensity information. Region-based filtering is then used to eliminate irrelevant anatomical structures and artifacts near image borders. Next, we segment the left and right lungs separately, compute their convex hulls, and merge them to maintain connectivity with nodules that may be attached to the pleural wall. Morphological operations such as erosion, dilation, and hole filling are used to refine lung masks, fill small cavities, and separate nodules from blood vessels. Finally, masking is applied to isolate lung areas by overlaying the binary mask onto the original scan frame. The preprocessing steps can also help suppress common artifacts such as noise and minor beam hardening through smoothing and lung masking (see Fig. 7).

## 5.3. Model training and evaluation

In this section, we present the hardware and software environments, as well as the hyperparameters for model training and metrics for model evaluation.

### 5.3.1. Environment configuration

The hardware and software setup for training and evaluation of our proposed model and other models are summarized in Table 1 for reproducibility.

All models are trained and compared on this hardware setup using Float32 (full precision), ensuring that the reported improvements reflect model advances. For deployment, the model also supports Float16 acceleration with minimal accuracy loss. This trade-off is discussed further in Section 5.9.

We employ the AdamW optimizer with a learning rate of 0.002 and a momentum of 0.9. Every model is trained for 200 epochs to ensure full convergence and the best model is saved based on its performance on the validation dataset.

<caption>**Table 1**</caption>

Hardware and software platform configuration for reproducibility of training and benchmarking results.

<table><thead><tr><td>Category</td><td>Details</td></tr></thead><tbody><tr><td>GPU</td><td>NVIDIA A100 80 GB PCIe</td></tr><tr><td>CPU</td><td>Intel® Xeon® Gold 6300 @ 2 GHz</td></tr><tr><td>RAM</td><td>2 TB</td></tr><tr><td>Operating System</td><td>Ubuntu 24.04</td></tr><tr><td>Python</td><td>3.9.19</td></tr><tr><td>PyTorch</td><td>2.0.1</td></tr><tr><td>CUDA</td><td>11.8</td></tr></tbody></table>

### 5.3.2. Evaluation metrics

We evaluate the performance of LNA-Net across two tasks: lung nodule detection and nodule malignancy classification, using standard deep learning metrics: accuracy, precision, recall, F1 score, and mean Average Precision (mAP).

$$ \text{Accuracy (Acc.)} = \frac{\text{TP} + \text{FP}}{\text{TP} + \text{TN} + \text{FP} + \text{FN}} \qquad (14) $$

$$ \text{Precision (P)} = \frac{\text{TP}}{\text{TP} + \text{FP}} \qquad (15) $$

$$ \text{Recall (R)} = \frac{\text{TP}}{\text{TP} + \text{FN}} \qquad (16) $$

$$ \text{F1 Score (F1)} = \frac{2 \times P \times R}{P + R} \qquad (17) $$

$$ \text{Average Precision (AP)} = \int_{0}^{1} p(r), dr \qquad (18) $$

$$ \text{mean Average Precision (mAP)} = \frac{1}{m} \sum_{i=1}^{m} AP_{i} \qquad (19) $$

where p(r) is the precision as a function of recall. In these equations, TP represents true positives, FP stands for false positives, TN denotes true negatives, and FN indicates false negatives. The mAP is computed by averaging the AP values across multiple classes, in this paper, only one class (nodule) is present hence the mAP is the same as the AP.

* For pulmonary nodule detection, mAP is an important metric, as it measures how well bounding boxes are placed and predicted across confidence thresholds. However, precision and recall also matter, high recall ensures nodules are not missed (false negatives), and high precision ensures few false positives.

* For malignancy classification, F1-score is very important because it balances false negatives (missing a malignant case) and false positives (misclassifying benign as malignant), which both have clinical consequences.
<caption>**Table 2**</caption>

Performance comparison of SOTA real-time object detection models on the LUNA16 lung nodule dataset. Metrics include mean average precision (mAP), precision (P), recall (R), F1-score (F1), parameter count (#Param., in millions), computational cost (GFLOPs), and inference speed (ms/image, Float32).

<table><thead><tr><th>Model</th><th>mAP</th><th>P</th><th>R</th><th>F1</th><th>#Param. (Total)</th><th>GFLOPs</th><th>Speed (ms/img)</th></tr></thead><tbody><tr><td>Faster R-CNN [10]</td><td>75.1%</td><td>80.1%</td><td>70.2%</td><td>74.9%</td><td>41.5M</td><td>134.4</td><td>22.2</td></tr><tr><td>RT-DETR [83]</td><td>80.1%</td><td>83.0%</td><td><strong>78.3%</strong></td><td><strong>80.6%</strong></td><td>32.0M</td><td>103.4</td><td>5.9</td></tr><tr><td>YOLOv3 [84]</td><td>74.3%</td><td>82.1%</td><td>70.3%</td><td>75.8%</td><td>103.7M</td><td>282.2</td><td>7.6</td></tr><tr><td>YOLOv3-tiny [85]</td><td>73.2%</td><td>82.8%</td><td>68.8%</td><td>75.2%</td><td>12.1M</td><td>18.9</td><td>3.6</td></tr><tr><td>YOLOv5n [86]</td><td>79.3%</td><td>82.9%</td><td>74.2%</td><td>78.3%</td><td>2.5M</td><td>7.1</td><td>4.9</td></tr><tr><td>YOLOv6 [87]</td><td>75.4%</td><td>80.3%</td><td>72.0%</td><td>75.9%</td><td>4.2M</td><td>11.8</td><td>3.9</td></tr><tr><td>YOLOv8n [57]</td><td>79.0%</td><td>81.4%</td><td>74.0%</td><td>77.5%</td><td>3.0M</td><td>8.1</td><td>4.3</td></tr><tr><td>YOLO11n [57]</td><td>75.7%</td><td>77.3%</td><td>73.0%</td><td>75.1%</td><td>2.6M</td><td>6.3</td><td>3.5</td></tr><tr><td>YOLOv9t [88]</td><td>77.7%</td><td>78.3%</td><td>72.5%</td><td>75.3%</td><td><strong>1.9M</strong></td><td>7.6</td><td>5.0</td></tr><tr><td>YOLOv10n [22]</td><td>79.1%</td><td>78.8%</td><td>76.4%</td><td>77.6%</td><td>2.7M</td><td>8.2</td><td>3.6</td></tr><tr><td>YOLOv12n [89]</td><td>76.2%</td><td>78.1%</td><td>73.5%</td><td>75.7%</td><td>2.6M</td><td>6.3</td><td>2.3</td></tr><tr><td>YOLOv13n [90]</td><td>77.1%</td><td>77.7%</td><td>72.5%</td><td>75.0%</td><td>2.4M</td><td><strong>6.2</strong></td><td>5.1</td></tr><tr><td>LNA-Net (Ours)</td><td><strong>82.5%</strong></td><td><strong>83.7%</strong></td><td><strong>77.0%</strong></td><td><strong>80.2%</strong></td><td>3.0M</td><td>8.3</td><td><strong>1.9</strong></td></tr></tbody></table>

We also utilize Precision-Recall (PR) curves for the detection task, which plot precision as a function of recall across varying confidence thresholds. These curves provide insight into the trade-off between detecting more nodules (recall) and avoiding false alarms (precision). The area under the PR curve is equivalent to the AP score.

For the classification task, we also use confusion matrices, which provide a detailed breakdown of prediction errors and support clinical interpretation.

## 5.4. Experimental results: Lung nodule detection

We trained and compared various models, with a primary focus on comparison with SOTA real-time models, including the YOLO [15] family and the recent Real-Time Detection Transformer (RT-DETR) [83].

We evaluate the models for lung nodule detection using mAP, precision, and recall. mAP is the primary metric in target detection problems, assessing the model's ability to accurately detect and localize nodules across varying Intersection over Union (IoU) thresholds, directly impacting subsequent malignancy classification and treatment planning. Therefore the model's ability to accurately localize nodules within the image is crucial. Precision measures how many of the detected nodules are true positives, i.e., how many of the predicted bounding boxes actually correspond to a true nodule, reflecting the model's ability to minimize false positives. False positive reduction is an important process in lung nodule detection [43]. Recall emphasizes the model's ability to detect all nodules, reducing the risk of missed cases. High recall ensures that the model is sensitive to detecting all nodules, minimizing the risk of missed detections (false negatives). Together, these metrics provide a holistic evaluation of the model's performance in detecting lung nodules for further classification in the next step 5.5.

We observe that two-stage detectors such as Faster R-CNN [10] fall short in inference speed and suffer from high floating point operations (FLOPs). Earlier iterations of YOLO, including YOLOv3 [84] and YOLOv6 [87], deliver suboptimal mAP for lung nodule detection compared to more recent and popular models like YOLOv5 [86] and YOLOv8 [57]. While YOLOv9t achieves minimal parameters, its 77.7% mAP lags behind YOLOv5 and YOLOv8 both of which exceed 79% but still have slower inference speeds compared to the latest YOLOv10 [22], which surpasses prior iterations in speed thanks to the elimination of NMS post-processing, albeit with a slightly lower mAP. YOLOv10 delivered the highest recall / sensitivity with generally high mAP among all convolution-based approaches, with the fastest speed and is the best baseline to build upon. Transformer-based RT-DETR [83] achieves the highest mAP and recall but incurs significantly higher computational costs due to its large number of parameters and GFLOPs, making it less suitable for real-time applications.

Inference speeds are measured in milliseconds per image (ms/img) on NVIDIA A100 GPU, a premium-grade hardware. However, testing on lower-end or edge processors would likely reveal more pronounced speed differences. The proposed LNA-Net surpasses all compared models in mAP, precision, and recall, while maintaining reasonable parameters and computational complexity, striking the balance. Crucially, it achieves the fastest inference speed at 1.9 ms/img, nearly twice as fast as YOLOv10, even before applying hardware acceleration techniques. Detailed results are presented in Table 2 (For all benchmarks, we report full Float32 inference results for all models on NVIDIA A100 GPU, unless specified otherwise). The PR curve in Fig. 9 further highlights LNA-Net's superior performance, as indicated by its higher area under the curve (AUC) of the green curve. LNA-Net outperforms the YOLO models and even surpasses the RT-DETR model, while being 11 times lighter in memory usage and 12.5 times more efficient in computational complexity. Fig. 8 presents the prediction results. The first row displays four images from the test set with their corresponding ground truth annotations. The second row shows detection results from YOLOv10n, while the third row illustrates results from the proposed LNA-Net. LNA-Net demonstrates a lower false negative rate, as shown in the first column, where YOLOv10n fails to detect a nodule that LNA-Net successfully identifies. Additionally, LNA-Net proves more robust in reducing false positives. For instance, in the second and third columns, YOLOv10n detects an extra nodule that is in fact a false positive, showcasing LNA-Net's robustness in false positive reduction, explaining the higher precision score. Moreover, LNA-Net exhibits higher and more centralized attention on nodules, further enhancing its detection and classification performance, as highlighted in the prediction comparisons.

Lung nodules exhibit substantial variability in shape, size, and density, posing a significant challenge for automated detection systems. As shown in Fig. 6, more than 20% of the annotated nodules in the dataset fall within the 3–5 mm range, making the accurate detection of small nodules a crucial benchmark for evaluating clinical viability. To assess the robustness of the proposed model under this constraint, we evaluate all detection models on a filtered subset of the test set containing only small nodules (≤4 mm).

Table 3 summarizes the comparative performance of real-time object detection models under this setting. We observe a substantial performance drop across all baseline models, consistent with the increased difficulty of detecting small and ambiguous targets. In contrast, LNA-Net maintains a high mAP of 81.4% and achieves an F1-score of 80.0%, outperforming all competitors. These results suggest that LNA-Net is more resilient to the challenges posed by subtle, low-contrast targets, exhibiting both improved false positive and false negative suppression. This is further validated by the PR curves in Fig. 10, where LNA-Net achieves the highest AUC, reflecting superior precision-recall trade-offs for small nodule detection.
<figure><img src="image_10.png" /><figcaption>Fig. 8. Qualitative comparison of detection results between YOLOv10 and LNA-Net on four CT scan slices. Heatmaps are visualized using EigenCAM [91] for better interpretability, illustrating the attention focus during nodule detection. The heatmaps are extracted from the same layers shown in Fig. 2.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 9. Precision-Recall (PR) curves for different object detection models in lung nodule detection. LNA-Net achieves a larger AUC, indicating higher overall detection performance compared to competing approaches.</figcaption></figure>

<caption>Table 3</caption>

Performance comparison of SOTA real-time object detection models for small lung nodules (≤4 mm) in the LUNA16 dataset. Metrics include mAP, P, R, and F1-score.

<table><thead><tr><th>Model</th><th>mAP</th><th>P</th><th>R</th><th>F1</th><th>#Param.<br>(Total)</th><th>GFLOPs<br>(Total)</th></tr></thead><tbody><tr><td>YOLOv3 [84]</td><td>73.3%</td><td>81.9%</td><td>71.0%</td><td>76.1%</td><td>103.7M</td><td>282.2</td></tr><tr><td>YOLOv5n [86]</td><td>77.9%</td><td>80.8%</td><td>75.2%</td><td>77.9%</td><td>2.5M</td><td>7.1</td></tr><tr><td>YOLOv8n [57]</td><td>76.9%</td><td>80.0%</td><td>74.2%</td><td>77.0%</td><td>3.0M</td><td>8.1</td></tr><tr><td>YOLOv10n [22]</td><td>78.3%</td><td>79.7%</td><td>77.0%</td><td>78.3%</td><td>2.7M</td><td>8.2</td></tr><tr><td>LNA-Net (Ours)</td><td>81.4%</td><td>82.5%</td><td>77.7%</td><td>80.0%</td><td>3.0M</td><td>8.3</td></tr></tbody></table>
<figure><img src="image_12.png" /><figcaption>**Fig. 10.** Precision-Recall (PR) curves for detection of small nodules (≤4 mm). LNA-Net maintains the highest AUC, demonstrating superior sensitivity for small nodule detection.</figcaption></figure>

<caption>**Table 4**</caption>

Performance comparison of lightweight SOTA real-time classification models on the Lung Nodule Malignancy dataset. Metrics include accuracy (Acc.), P, R, F1, #Param. (in millions), GFLOPs, and inference speed (ms/image).

<table><thead><tr><th>Model</th><th>Acc.</th><th>P</th><th>R</th><th>F1</th><th>#Param. (Total)</th><th>GFLOPs</th><th>Speed (ms/img)</th></tr></thead><tbody><tr><td>EfficientFormer [69]</td><td>91.0%</td><td>86.9%</td><td>88.8%</td><td>87.8%</td><td>11.4M</td><td>1.3</td><td>0.8</td></tr><tr><td>MobileViT [68]</td><td>94.8%</td><td>94.1%</td><td>91.8%</td><td>92.9%</td><td>4.9M</td><td>1.5</td><td>1.5</td></tr><tr><td>ResNet50 [61]</td><td>88.8%</td><td>84.7%</td><td>84.6%</td><td>84.6%</td><td>23.5M</td><td>4.1</td><td>0.7</td></tr><tr><td>ConvNeXt [65]</td><td>90.2%</td><td>94.7%</td><td>77.6%</td><td>85.3%</td><td>27.8M</td><td>4.5</td><td>1.2</td></tr><tr><td>MobileNetv3 [63]</td><td>94.8%</td><td>95.7%</td><td>90.0%</td><td>92.8%</td><td>1.5M</td><td>0.1</td><td>0.8</td></tr><tr><td>EfficientNetv2 [64]</td><td>94.3%</td><td><strong>96.4%</strong></td><td>87.8%</td><td>91.9%</td><td>20.2M</td><td>5.8</td><td>1.0</td></tr><tr><td>ShuffleNetv2 [62]</td><td>92.6%</td><td>92.3%</td><td>87.2%</td><td>89.7%</td><td><strong>1.3M</strong></td><td><strong>0.1</strong></td><td>0.8</td></tr><tr><td>YOLOv10-cls [22]</td><td>91.5%</td><td>92.2%</td><td>84.0%</td><td>87.9%</td><td>1.5M</td><td>3.4</td><td>0.3</td></tr><tr><td><strong>LNA-Net-cls (Ours)</strong></td><td><strong>95.1%</strong></td><td>93.3%</td><td><strong>93.4%</strong></td><td><strong>93.3%</strong></td><td>1.8M</td><td>3.6</td><td><strong>0.3</strong></td></tr></tbody></table>

## 5.5. *Experimental results: Lung nodule malignancy classification*

As previously discussed, this research not only aims to enhance nodule detection but also streamlines the malignancy classification task with the “Cls Switching” mechanism using the backbone with updated weights. To assess this, To assess this, we compare the performance of the LNA-Net backbone in classifier mode (LNA-Net-cls) against a range of lightweight SOTA models, including both transformer-based and convolutional architectures. Given the importance of high-speed batch processing in our clinical use case, we focus on lightweight SOTA classifiers.

While we report standard metrics such as accuracy, precision, recall, and F1-score, we note that accuracy alone can be misleading, especially under class imbalance. In malignancy classification, the clinical cost of errors is asymmetrical: false negatives (missed malignant nodules) risk delayed diagnosis and treatment, whereas false positives may lead to unnecessary invasive procedures and anxiety. Recall is therefore critical, ensuring high sensitivity to malignant cases, while precision reflects the model’s ability to reduce overdiagnosis. The F1-score, as the harmonic mean of precision and recall, is particularly important here as it captures the balance between these two competing objectives.

As shown in Table 4, transformer-based MobileViT [68] achieves strong performance (F1: 92.9%), but with a higher parameter count (4.9M) and slower inference time (1.5 ms/img). Among convolution-based models, MobileNetv3 [63] and ShuffleNetv2 [62] deliver high

accuracy and precision but differ in recall, with EfficientNetv2 [64] showing reduced recall (87.8%) despite achieving the highest precision (96.4%). ShuffleNetv2 is the most lightweight model (1.3M parameters, 0.1 GFLOPs), but its lower recall (87.2%) limits its clinical applicability.

YOLOv10-cls offers the fastest inference time (0.3 ms/img) but sacrifices classification performance, particularly in recall (84.0%) and F1 (87.9%). This suggests that its advantage for speed comes at the expense of diagnostic performance. In contrast, our proposed LNA-Net-cls achieves the highest overall performance across all metrics: accuracy (95.1%), recall (93.4%), and F1-score (93.3%), while matching YOLOv10-cls in speed (0.3 ms/img). This suggests that LNA-Net-cls strikes the optimal balance between diagnostic effectiveness and deployment efficiency.

These improvements can be attributed to the adaptive kernel attention mechanism (AKAM), which enhances feature aggregation and discriminative power without incurring significant computational cost. The confusion matrices in Fig. 11 highlight LNA-Net-cls’s ability to correctly identify malignant nodules (true positives) while minimizing both false positives and false negatives compared to SOTA convolution and transformer-based classifiers, while using the model’s backbone without the need of an additional classifier.

In summary, LNA-Net-cls offers a compelling solution for real-time malignancy classification: high sensitivity (recall) to detect critical cases, strong precision to avoid overdiagnosis, and an optimal F1-score that reflects robust clinical decision-making. This positions LNA-Net-cls
<figure><img src="image_13.png" /><figcaption>Fig. 11. Confusion Matrix for malignancy classification: (a) MobileNet (b) MobileViT (c) YOLOv10-cls (d) LNA-Net-cls.</figcaption></figure>

<caption>Table 5</caption>

Ablation study of LNA-Net components. Progressive integration of WIoU loss for bounding box regression, AKAM, and C2fCNB is evaluated using mAP, P, R, F1, #Param., GFLOPs, and speed.

<table><thead><tr><th>Model</th><th>mAP</th><th>P</th><th>R</th><th>F1</th><th>#Param.<br>(Total)</th><th>GFLOPs</th><th>Speed<br>(ms/img)</th></tr></thead><tbody><tr><td>Baseline</td><td>79.1%</td><td>78.8%</td><td>76.4%</td><td>77.6%</td><td>2.7M</td><td>8.2</td><td>3.6</td></tr><tr><td>+WIoU</td><td>80.2%</td><td>83.2%</td><td>76.7%</td><td>79.8%</td><td>2.7M</td><td>8.2</td><td>3.5</td></tr><tr><td>+WIoU+AKAM</td><td>81.8%</td><td>85.2%</td><td>76.9%</td><td>80.8%</td><td>3.0M</td><td>8.5</td><td>2.9</td></tr><tr><td>+WIoU+AKAM+C2fCNB</td><td>82.5%</td><td>83.7%</td><td>77.0%</td><td>80.2%</td><td>3.0M</td><td>8.3</td><td>1.9</td></tr></tbody></table>

as a strong candidate for practical deployment in resource-constrained diagnostic settings.

## 5.6. Ablation study: LNA-Net

We conducted an ablation study to assess the performance improvements of the proposed LNA-Net compared to the baseline model. The results are summarized in Table 5.

The first modification replaces the default CIoU loss with WIoUv2, and improved the model's robustness to low-quality samples often found in CT scans (e.g., motion blur, beam hardening), increasing mAP by 1.1% and significantly improving precision by 4.4%, a direct indication of false positive reduction. The recall also improved slightly, leading to a better F1-score.

Adding the AKAM block to the backbone led to a further mAP gain of 1.6%, with an additional 2% increase in precision and a marginal recall gain. This suggests that AKAM helps focus attention on nodule-relevant regions and suppresses background noise, thereby reducing both false positives and false negatives. Importantly, inference speed improved significantly (from 3.6 ms/img to 2.9 ms/img), despite a moderate increase in parameters and FLOPs. This is due to AKAM's efficient multi-kernel dilated convolution design, which captures multi-scale features with linear complexity, unlike attention-based modules

that scale quadratically [92]. The AKAM block thus provides both accuracy gains and computational efficiency.

Adding the C2fCNB block, a lightweight feature fusion module we proposed, led to the highest mAP (82.5%) and reduced inference time to 1.9 ms/img, the fastest among all tested configurations. This block enhances feature aggregation across scales, which helps in both reducing false negatives (reflected in the recall gain) and further consolidating correct predictions. Notably, this final configuration achieved a strong balance of precision and recall (F1 score of 80.2%), reinforcing LNA-Net's robustness in lung nodule detection.

Together, these components improve detection accuracy, speed, and clinical relevance, particularly by reducing false positives (high precision) and false negatives (high recall), as reflected in the F1-score. These results confirm that each component of LNA-Net contributes meaningfully: WIoU improves robustness to artifacts and noisy inputs; AKAM enhances multiscale feature extraction with high efficiency; and C2fCNB improves fusion for better final predictions. These improvements not only enhance overall detection performance but also provide more reliable inputs for downstream malignancy classification.

## 5.7. Ablation study: AKAM design

We empirically validate the design choices of the proposed AKAM block in this study. The first part of Table 6 demonstrates the impact of
<caption>**Table 6**</caption>

Ablation study of the AKAM block design. Compares the effects of different dilation rates, kernel size configurations, and convolution-attention combinations on detection performance and parameter efficiency.

<table><thead><tr><th></th><th>mAP</th><th>P</th><th>R</th><th>F1</th><th>#Param.</th></tr></thead><tbody><tr><td><strong>Dilation</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>[1, 1, 1]</td><td>76.9%</td><td>82.4%</td><td>73.8%</td><td>77.9%</td><td>221,440</td></tr><tr><td>[2, 2, 2]</td><td>79.9%</td><td>80.9%</td><td>73.5%</td><td>77.9%</td><td>221,440</td></tr><tr><td>[3, 3, 3]</td><td>77.7%</td><td>82.3%</td><td>74.9%</td><td>78.4%</td><td>221,440</td></tr><tr><td>[1, 2, 3]</td><td><strong>81.8%</strong></td><td><strong>85.2%</strong></td><td><strong>76.9%</strong></td><td><strong>80.8%</strong></td><td><strong>221,440</strong></td></tr><tr><td><strong>Kernel</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>[3]</td><td>77.0%</td><td>83.9%</td><td>70.1%</td><td>76.4%</td><td><strong>201,984</strong></td></tr><tr><td>[5]</td><td>75.8%</td><td>80.5%</td><td>70.4%</td><td>75.1%</td><td>206,080</td></tr><tr><td>[7]</td><td>76.4%</td><td>86.2%</td><td>65.9%</td><td>74.7%</td><td>212,224</td></tr><tr><td>[3, 5, 11]</td><td>77.5%</td><td>83.7%</td><td>69.6%</td><td>76.0%</td><td>239,872</td></tr><tr><td>[5, 7, 11]</td><td>79.8%</td><td>81.7%</td><td>74.9%</td><td>78.2%</td><td>250,112</td></tr><tr><td>[3, 5, 7]</td><td><strong>81.8%</strong></td><td><strong>85.2%</strong></td><td><strong>76.9%</strong></td><td><strong>80.8%</strong></td><td>221,440</td></tr><tr><td>[3, 7, 11]</td><td>77.6%</td><td>80.4%</td><td>72.8%</td><td>76.4%</td><td>246,016</td></tr><tr><td>[3, 5, 7, 11]</td><td>79.6%</td><td>80.7%</td><td>75.1%</td><td>77.8%</td><td>252,672</td></tr><tr><td><strong>Conv & Attention</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>[3, 5, 7] Conv</td><td>69.4%</td><td>70.9%</td><td>65.1%</td><td>67.9%</td><td>5,639,680</td></tr><tr><td>[3, 5, 7] DWConv</td><td>76.9%</td><td>82.4%</td><td>73.8%</td><td>77.9%</td><td>221,440</td></tr><tr><td>[3, 5, 7] ADDWConv</td><td><strong>81.8%</strong></td><td><strong>85.2%</strong></td><td><strong>76.9%</strong></td><td><strong>80.8%</strong></td><td>221,440</td></tr><tr><td>[3, 5, 7] ADDWConv, No SA.</td><td>78.3%</td><td>80.6%</td><td>76.1%</td><td>78.3%</td><td><strong>155,648</strong></td></tr></tbody></table>

adaptive dilation on the receptive field. The adaptive dilation [1,2,3] for the three parallel kernels outperforms fixed dilation (e.g., [1,1,1], [2,2,2], [3,3,3]) in terms of mAP, precision, and recall, showing improved detection across varying nodule sizes, as illustrated in Fig. 6. The adaptive dilation setup reaches the highest 81.8% mAP, 85.2% precision and 76.9% recall.

In the second part, we explore the effect of different kernel designs while keeping the adaptive dilation setup. While using a single kernel reduces parameters, it leads to significantly lower mAP and recall compared to multi-scale kernels. Larger kernels (e.g., size 11) may introduce higher background noise, hindering detection. The optimal kernel combination is [3,5,7], which achieves the highest performance with an mAP of 81.8%, precision of 85.2%, and recall of 76.9%. Multi-scale kernels like [3,5,11] or [3,7,11] underperform, as skipping consecutive kernel sizes reduces effectiveness. Moreover, introducing additional kernels beyond [3,5,7] increases parameters without improving detection performance.

In the third part, we evaluate different convolution types and the effect of spatial attention. Traditional convolution with multi-scale kernels yields low performance due to high parameter counts and low computational efficiency (69.4% mAP). Using DWConv significantly boosts performance (76.9% mAP) with a dramatic reduction in parameters (96%) from 5.6M to 221K. The highest performance is achieved by incorporating adaptive dilated depthwise convolution (ADDWConv) and spatial attention, yielding mAP of 81.8%, precision of 85.2%, and recall of 76.9%. Removing spatial attention reduces mAP by 3.5%, precision by 4.6%, and recall by 0.8%, emphasizing the importance of feature recalibration after multi-scale extraction.

These results confirm that the AKAM module, particularly the combination of adaptive dilation, multi-scale kernels, and spatial attention, significantly enhances performance while maintaining low computational complexity. The three parallel kernels demonstrated optimal performance for nodule detection, while the modular design allows for future kernel extensions and adaptation to other detection tasks.

## 5.8. Ablation study: Backbone attention mechanism

We conduct an ablation study to evaluate the proposed AKAM attention mechanism with several SOTA and popular alternatives, to assess its relative performance and efficiency in the specific lung nodule detection task. For consistency, the architecture remains identical

<caption>**Table 7**</caption>

Comparison of different attention mechanisms integrated into the baseline detector. Evaluates mAP, P, R, F1, attention parameter count, and total GFLOPs to assess efficiency-accuracy trade-offs.

<table><caption>**Table 8**</caption><thead><tr><th>Attention</th><th>mAP</th><th>P</th><th>R</th><th>F1</th><th>#Param. (Att.)</th><th>GFLOPs (Total)</th></tr></thead><tbody><tr><td>SE [26]</td><td>78.0%</td><td>85.1%</td><td>69.8%</td><td>76.7%</td><td>512</td><td>8.3</td></tr><tr><td>CBAM [27]</td><td>78.3%</td><td>77.4%</td><td>75.7%</td><td>76.5%</td><td>8,563</td><td>8.3</td></tr><tr><td>Shuffle [62]</td><td>76.1%</td><td>80.3%</td><td>71.7%</td><td>75.8%</td><td>96</td><td>8.3</td></tr><tr><td>SK [28]</td><td>80.1%</td><td>80.9%</td><td>75.1%</td><td>77.9%</td><td>5.6M</td><td>12.7</td></tr><tr><td>SimAM [93]</td><td>77.1%</td><td>79.7%</td><td>71.9%</td><td>75.6%</td><td>0</td><td>8.3</td></tr><tr><td>GAM [29]</td><td>77.9%</td><td>80.0%</td><td>72.8%</td><td>76.2%</td><td>435,456</td><td>8.6</td></tr><tr><td>MobileViT [94]</td><td>78.2%</td><td>79.3%</td><td>74.3%</td><td>76.7%</td><td>197,633</td><td>8.4</td></tr><tr><td>PSA [22]</td><td>79.1%</td><td>81.5%</td><td>77.0%</td><td>79.2%</td><td>249,728</td><td><strong>8.2</strong></td></tr><tr><td>MSCA [34]</td><td>80.6%</td><td>79.5%</td><td>76.5%</td><td>78.0%</td><td>93,952</td><td>8.4</td></tr><tr><td>PKI [35]</td><td>80.3%</td><td>84.3%</td><td>74.9%</td><td>79.3%</td><td>947,968</td><td>9.0</td></tr><tr><td><strong>AKAM (Ours)</strong></td><td><strong>81.8%</strong></td><td><strong>85.2%</strong></td><td><strong>76.9%</strong></td><td><strong>80.8%</strong></td><td>221,440</td><td>8.5</td></tr></tbody></table>

Performance comparison of AKAM and alternative attention mechanisms for detecting small lung nodules (≤4 mm). Metrics include mAP, P, R, and F1-score.

<table><thead><tr><th>Attention</th><th>mAP</th><th>P</th><th>R</th><th>F1</th><th>#Param. (Att.)</th><th>GFLOPs (Total)</th></tr></thead><tbody><tr><td>SE [26]</td><td>78.3%</td><td>79.7%</td><td>79.0%</td><td>79.3%</td><td>512</td><td>8.3</td></tr><tr><td>CBAM [27]</td><td>78.7%</td><td>76.9%</td><td>77.4%</td><td>77.1%</td><td>8,563</td><td>8.3</td></tr><tr><td>PSA [22]</td><td>78.3%</td><td>79.7%</td><td>77.0%</td><td>78.3%</td><td>249,728</td><td>8.2</td></tr><tr><td>PKI [35]</td><td>79.3%</td><td>84.0%</td><td>74.2%</td><td>78.8%</td><td>947,968</td><td>9.0</td></tr><tr><td><strong>AKAM (Ours)</strong></td><td><strong>80.3%</strong></td><td><strong>84.4%</strong></td><td><strong>76.7%</strong></td><td><strong>80.4%</strong></td><td>221,440</td><td>8.5</td></tr></tbody></table>

across all experiments, and we isolate the effect of the attention module by excluding the C2fCNB block.

As summarized in Table 7, AKAM outperforms all other attention mechanisms in terms of mAP (81.8%), precision (85.2%), recall (76.9%), and F1-score (80.8%). Notably, it is the only attention module to achieve an F1-score above 80%, indicating superior balance between false positive and false negative reduction.

Self-attention-based mechanisms such as PSA [22] and Mobile-ViT [94] improve mAP over classical approaches, with PSA reaching 79.1%, thanks to better feature contextualization. Multi-scale attention methods like MSCA [34] and PKI [35] also perform well, particularly in recall, likely due to their stronger multi-scale feature representations, which are crucial for detecting nodules of varying sizes and appearances. However, PKI's higher parameter count (947k) makes it less suitable for real-time or resource-constrained settings.

AKAM surpasses all of these alternatives not only in mAP and F1 but also in maintaining low computational cost (8.5 GFLOPs), close to the lightest attention modules. This highlights its ability to provide high detection quality without compromising efficiency, a key requirement in clinical deployments. This makes AKAM an ideal choice for real-time detection tasks. The PR curves shown in Fig. 12 further confirm AKAM's superiority, with the green line representing the highest AUC.

The qualitative results in Fig. 13 further illustrate AKAM's attention behavior. We compare activation maps generated by EigenCAM for CBAM, SE, and AKAM. While SE shows higher precision by suppressing false positives, its heatmap lacks strong focus on the true nodule region. In contrast, CBAM attends to the correct area but also activates irrelevant regions, leading to false positives. AKAM exhibits a more centralized and confident focus on the nodule itself, aligning with its superior F1 and mAP.

In summary, AKAM demonstrates state-of-the-art performance among both classic and recent attention mechanisms in the context of lung nodule detection. It effectively balances sensitivity and specificity, delivering high precision and recall while remaining computationally lightweight, making it highly suitable for deployment in real-time medical imaging workflows.
<figure><img src="image_14.png" /><figcaption>**Fig. 12.** Precision-Recall (PR) curves from the ablation study on attention mechanisms. Standard methods (SE, CBAM) and more recent multi-scale attention designs (MSCA, PKI) are compared. The proposed AKAM consistently achieves the highest AUC.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>**Fig. 13.** Performance comparison of AKAM over SEA and CBAM using EigenCAM extracted right after the backbone attention layer. The results show that while SEA reduces false positive (higher precision) compared to CBAM, both have lower attention compared to AKAM's more centralized attention on the detected nodule.</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>**Fig. 14.** LNA-Net FP16 inference runtimes across various hardware, measured for hardware-accelerated deployment optimization (not for FP32 benchmarking).</figcaption></figure>
<figure><img src="image_17.png" /><figcaption>Fig. 15. Precision-Recall (PR) curves for the ablation study on attention mechanisms in small nodule detection (≤4 mm). AKAM outperforms all baseline attention modules, maintaining strong performance for the most challenging nodule sizes.</figcaption></figure>

When evaluating attention mechanisms on small nodule detection (Table 8), we observe that traditional approaches like SE and CBAM, which are widely used in general object detection, perform poorly in this context. Their F1-scores remain below 80%, with CBAM showing a notable drop to 77.1%, indicating weaker performance on small nodules.

More advanced attention mechanisms, such as PSA (self-attention) and PKI (multi-scale), improve mAP and precision. For example, PKI achieves a relatively high precision of 84.0% and mAP of 79.3%, but with a significantly higher parameter count of nearly 1M, indicating increased model complexity. However, these methods still struggle to maintain balanced recall, leading to lower F1-scores compared to our method.

In contrast, our proposed AKAM module achieves the best overall performance, with an F1-score of 80.4%, mAP of 80.3%, and the highest precision of 84.4%, all while maintaining a lightweight footprint of only 221k parameters. This demonstrates that AKAM not only improves localization and classification of small nodules but also does so efficiently, outperforming both conventional and more recent complex attention mechanisms in both accuracy and model efficiency. Fig. 15 also shows higher AUC with AKAM.

## 5.9. Deployment acceleration

While all core performance benchmarks in this paper were conducted on the NVIDIA A100 GPU using full FP32 precision for fairness and reproducibility, this section is not intended for benchmarking. Instead, it demonstrates the deployability of LNA-Net in resource-constrained and edge computing scenarios. Such deployment considerations are important because cloud-based inference on data center GPUs is not always feasible, either due to patient data privacy regulations or due to financial constraints.

To evaluate practical deployment, we optimized LNA-Net for diverse hardware platforms using industry-standard inference toolchains: OpenVINO for Intel CPUs, TensorRT for NVIDIA GPUs, ONNX Runtime for ARM-based edge processors, and CoreML for Apple's ARM-based System-on-Chips (SoCs). These toolchains adapt the model's computational graph for efficient execution across heterogeneous architectures.

For storage optimization, we applied INT8 palettization to model weights only, reducing the memory footprint from 6.3 MB to 3.1 MB.

At runtime, models were executed in Float16 precision to balance speed and accuracy—this format is widely used in AI deployments and is also required by Apple's Neural Engine. As expected, FP16 inference incurs a slight accuracy drop compared to FP32, with mAP = 82.1%, Precision = 81.1%, and Recall = 79.7%, which remains well within acceptable bounds compared to FP32.

Fig. 14 illustrates the inference speed per image on several hardware platforms. On the premium-grade A100 GPU, the performance gain is relatively modest, with inference time reduced to 1.2 ms from 1.9 ms when using the raw PyTorch model. However, on edge devices, the improvements are far more significant. Using CoreML on the Apple M1 SoC and leveraging the Neural Engine, LNA-Net achieves 2.8 ms per image (357 FPS), an 84× speed-up over CPU-only inference on the same device. This means that processing 40,000 CT scans [5] would take under two minutes entirely on a mobile-class processor. Even the four-year-old A14 SoC (iPhone 12) achieves 9.0 ms per image (111 FPS), showing that the model retains real-time capability on older hardware.

In summary, our approach ensures efficient deployment across a wide range of hardware platforms, offering real-time inference capabilities and low memory requirements, making LNA-Net suitable for both high-performance servers and edge devices.

## 6. Conclusion

In conclusion, LNA-Net represents a significant advancement in the field of lung nodule detection and malignancy classification. By introducing the novel Adaptive Kernel Attention Module (AKAM), we enhance the model's ability to efficiently extract features from small, challenging targets, ensuring high sensitivity in the detection of malignant nodules. The integration of a customized C2fCNB block further strengthens feature fusion, enabling the unified pipeline to eliminate the need for a separate classifier while maintaining high performance.

Our evaluations on the LUNA16 dataset demonstrate that LNA-Net outperforms SOTA models, achieving a 3.4% improvement in mAP@50 for the nodule detection task, and significant enhancements in recall (+9.4%) and accuracy (+3.6%) for malignancy classification, while maintaining real-time efficiency, a crucial factor for large-scale deployment in clinical settings. The combination of these features: superior performance, computational efficiency, and ease of deployment positions LNA-Net as a valuable tool for assisting radiologists in the timely
and accurate diagnosis of lung cancer, ultimately contributing to better patient outcomes.

Furthermore, the model's lightweight architecture ensures that it is not only suitable for high-performance servers but also deployable on edge devices, achieving as fast as 2.6 ms per image on mobile SoCs, providing flexibility for diverse clinical environments. With its potential for real-time analysis and minimal memory requirements, LNA-Net stands as a promising solution to accelerate the detection and classification of lung nodules, supporting the broader effort to reduce cancer-related mortality worldwide.

## CRediT authorship contribution statement

**Zhifan Song:** Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Conceptualization. **Bin Song:** Writing – review & editing, Validation, Resources, Investigation, Conceptualization. **Abd Al Rahman M. Abu Ebayyeh:** Writing – review & editing, Validation, Supervision, Investigation.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data availability

Data will be made available on request.

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