## 1 Introduction
The weight parameters of the SE-Xception model are shared with the CBWO-CNN. [PERSON] et al. [10] trained a You Only Look Once version 3 (YOLOv3) network with multi-layer feature fusion on a publicly available dataset to identify lung nodules. Subsequently, a CNN underwent training to distinguish between benign and malignant pulmonary nodules. Adopting the concept of ensemble learning, the combined confidence probabilities of the aforementioned models and the labels within the training dataset were utilized as data features to construct a Logistic regression model. The authors tested their method on two separate test datasets (one public and one private), and the combined confidence probabilities outputted by the two models were amalgamated into the established logistic regression model to ascertain the nature of benign and malignant pulmonary nodules. The YOLOv3 network was trained to detect chest CT images within the test set. The author in [11] suggested utilizing augmentation techniques to enhance the precision of a CNN. Her method involved manipulating existing training data through transformations like scaling, contrast adjustments, and rotation to identify suitable training samples. [PERSON] et al. [12] proposed a machine-learning technique to categorize lung images obtained from CT scans. They proposed two innovative characteristics: Gabor energy and Gabor entropy, alongside five grey-level co-occurrence matrix (GLCM) features. They stated that the Gabor energy and Gabor entropy attributes are unique and contribute to enhancing the performance of the classification system, resulting in improved accuracy. [PERSON] et al. [13] proposed to use CNN and DenseNet methodologies to assist in identifying and categorizing lung cancer, as both methods have been proven effective in pattern recognition and medical imaging across different domains. The study utilized the radiology images of lung cancer patients to compile a dataset of medical lung images. [PERSON] et al. (2014) introduced a computer-aided diagnosis framework, titled CSO-CADLCC, for the classification of lung cancer. To begin, the CSO-CADLCC method undergoes an initial data preprocessing phase utilizing a Gabor filtering-based technique for noise removal. Subsequently, feature extraction from the processed images was carried out using the NASNetLarge model. This step was succeeded by the application of the CSO algorithm combined with a weighted extreme learning machine (WELM) model for lung nodules classification. Finally, the CSO algorithm was employed to fine-tune the parameters of the WELM model, resulting in enhanced classification performance. The experimental validation of the CSO-CADLCC framework was conducted using the Early Lung Cancer Action Program (ELCAP) Public Lung Image Database. [PERSON] et al. (2015) proposed some transfer learning methods to intelligently classify lung nodules. Various CNN architectures (including MobileNet, DenseNet169, Visual Geometry Group network with 16 layers (VGG16), and Visual Geometry Group network with 19 layers (VGG19)) were used to configure the input and output layers of a dataset consisting of chest CT scan images. The dataset comprises normal chest CT scans alongside images from two types of lung cancer, namely squamous and adenocarcinoma. [PERSON] et al. (2016) proposed an approach named Segmentation Attention Asymmetric Convolution Network (SAACNet) coupled with a Gradient Boosting Machine (GBM). Firstly, SAACNet incorporates Asymmetric Convolution (AC) to enhance feature extraction and robustness against object transformations, thereby boosting network performance. Secondly, the segmentation Attention Asymmetric Convolution (SAAC) block effectively captures the detailed multiscale spatial information while dynamically recalibrating attention weights across channels. Thirdly, GBM integrates nodule size, cropped nodule pixels, and depth features acquired by SAACNet to enhance the overall predictive accuracy. The authors conducted experiments using the LUNA16 dataset to validate the proposed methodology.