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 carcinoma, large cell carcinoma, or adenocarcinoma. Unlike conventional methods, our approach not only achieves highly accurate classification of lung cancer subtypes but also incorporates clinically validated interpretability features to ensure alignment with medical diagnostics. Our model trained on a comprehensive dataset of CT images achieved an overall accuracy of 93.06%. This performance demonstrates the model’s robustness in detecting even subtle malignancies, with strong precision, recall, and F1-scores across all cancer types. Including interpretable Grad-CAM visualizations ensures reliability and transparency, aiding clinicians in understanding the model’s predictions. This innovative method demonstrates the potential to revolutionize early lung cancer detection and improve patient survival rates by combining state-of-the-art accuracy with explainability tailored for clinical application.
© 2025. The Author(s).