Explainable AI for lung cancer detection via a custom CNN on CT images

Lung cancer, which claims 1.8 million lives annually, is still one of the leading causes of cancer-related deaths globally. Patients with lung cancer frequently have a bad prognosis because of late-stage detection, which severely limits treatment options and decreases survival rates. Early detection is essential for better outcomes, but traditional CT image analysis is time-consuming, prone to error, and relies on subjective judgments. To overcome these issues, we propose a custom convolutional neural network (CNN) combined with explainable AI (XAI) techniques, particularly gradient-weighted class activation mapping (Grad-CAM). This approach is intended to reliably classify lung cancer into squamous cell…

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  Prince Sultan University