A computer‐aided diagnosis (CAD) system based on convolutional neural networks for lung cancer diagnosis from 2D [¹⁸F]‐ PET/CT images

Objective This study aims to automatically classify lung conditions into normal, non‐small cell lung cancer (NSCLC), and small cell lung cancer (SCLC) using [ 18 F] FDG PET/CT images and deep learning. Methods PET/CT scans from 146 patients (1974 scans) were retrospectively analyzed using two strategies: (1) transfer learning with pre‐trained CNNs, and (2) a custom CNN (Res‐SE Net) incorporating residual and squeeze‐and‐excitation (SE) modules. A patient‐based data splitting approach was used to avoid data leakage. Models were trained and validated at the scan level and evaluated at the patient level using majority voting. Grad‐CAM was employed to generate lesion‐localization heatmaps. Results Among the seven evaluated CNN models, the proposed Res‐SE Net demonstrated superior performance, achieving an accuracy of 91.67% and a sensitivity of 92.00% in detecting NSCLC, and an accuracy of 90.14% with a sensitivity of 90.00% for distinguishing SCLC cases. When tested on an external dataset, the model attained an accuracy of 98.00% in binary classification (Normal vs. Cancer). In the three‐class classification task, the model achieved an accuracy of 73.02% for NSCLC and 66.26% for SCLC. Conclusion These findings demonstrate the potential of Res‐SE Net architecture for accurate multi‐class lung cancer classification using [18F] FDG PET/CT images.