Automated classification and explainable AI analysis of lung cancer stages using EfficientNet and gradient-weighted class activation mapping

Precise classification of lung cancer stages based on CT images remains a significant challenge in oncology. This is vitally necessary for determining prognosis and creating practical treatment plans. Traditional methods mainly rely on human interpretation, which can be inconsistent and prone to fluctuation. To overcome these limitations an automated deep learning model based on the EfficientNet-B0 based architecture is proposed. Explainable AI features enhanced through Gradient-weighted Class Activation Mapping (Grad-CAM) help further boost this model. Training of the model was conducted with 1,190 CT scans from the IQ-OTH/NCCD dataset. All the images fell into the benign, malignant, and normal categories. The suggested technique performs remarkably well, reaching 99% accuracy, 99% precision, and recall rates of 96% for benign cases, 99% for malignant cases, and 100% for normal occurrences. Grad-CAM makes the model more interpretable and transparent by providing visual explanations of its results. It identifies the most important regions in the scans that significantly contribute to the classification results. Apart from contributing to the field of medical image analysis, accurate precision and complete explanations also bring automated diagnosis systems credibility and reliability.