# Design of a Lung Lesion Target Detection Algorithm Based on a Domain-Adaptive Neural Network Model
###### Abstract  
This study developed a novel domain-adaptive neural network framework, CNDAD--Net, for addressing the challenges of lung lesion detection in cross-domain medical image analysis. The proposed framework integrates domain adaptation techniques into a classical encoding-decoding structure to align feature distributions between source and target domains. Specifically, a \"Generative Adversarial Network\" GAN-based domain discriminator is utilized for the iterative refinement of feature representations to minimize cross-domain discrepancies and improve the generalization capability of the model. In addition, a novel Cross-Fusion Block (CFB) is proposed to implement multi-scale feature fusion that facilitates the deep integration of 2D, 3D, and domain-adapted features. The CFB achieves bidirectional feature flow across dimensions, thereby improving the model's capability to detect diverse lesion morphologies while minimizing false positives and missed detections. For better detection, coarse-grained domain adaptation is implemented by MMD for further optimization. It integrates a module inspired by a CycleGAN for the process to generate high-resolution images on low-quality data. Using the Lung Nodule Analysis (LUNA16) dataset, the test was conducted and its experimental result was compared with that of previous standard methods such as Faster R-CNN and YOLO, yielding mAP 0.889, recall at 0.845 and the F1-score at 0.886. This work, with a novel CNDAD--Net model, lays down a solid and scalable framework for the precise detection of lung lesions, which is extremely critical for early diagnosis and treatment. The model has prospects and is capable of being extended in future to multimodal imaging data ad real-time diagnostic scenarios, and can help in further developing intelligent medical image analysis systems. lung lesion; target detection; transfer learning; domain adaption 2020  
From predicting disease risks, to augmenting clinical diagnostics, AI is increasingly becoming an indispensable tool for coping with the traditional challenges inherent in medicine [8]. In the field of medical image analysis, traditional machine learning methods have played an important role in lung lesion detection, particularly in early computer-aided diagnosis (CAD) systems. Common machine learning methods include Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost). These methods typically rely on manually extracted features, such as Gray-Level Co-occurrence Matrix (GLCM), morphological features, and histogram equalization features, followed by classification for lung lesion detection and categorization. However, the performance of these methods heavily depends on the quality of feature engineering. Manually designed features often fail to comprehensively capture the complex information in medical images, especially when dealing with large datasets or complex feature distributions, which may lead to poor generalization. Additionally, machine learning methods face significant challenges in handling domain shifts in data distributions, often resulting in a considerable performance drop when applied to target domain data [9]. With the advancement of deep learning, convolutional neural network (CNN)-based methods have gradually replaced traditional handcrafted feature extraction approaches, allowing models to automatically learn multi-level feature representations and thereby enhancing the robustness and accuracy of lung lesion detection. In recent years, numerous state-of-the-art object detection methods have emerged, such as the YOLO series, Faster R-CNN, and SSD [10]. These methods achieve fast and high-precision object detection by jointly optimizing feature extraction networks and detection heads, making them suitable for a wide range of complex scenarios [11]. However, in the field of medical imaging, traditional object detection methods still face certain limitations, particularly when dealing with domain shifts in data distributions.