Lung Cancer Detection from Histopathological Images using Custom CNN With XAI Techniques
Abstract
One of the leading causes of cancer-related mortality worldwide is lung cancer. Improving patient survival requires early and precise diagnosis. Recent developments in deep learning make it possible to analyse histopathological lung pictures automatically. Because most deep learning models are opaque and considered black-box systems, their application in clinical practice is constrained. An explainable deep-learning framework for the categorisation of lung cancer is presented in this article. Gradient-weighted Class Activation Mapping (Grad-CAM) visualisation and a custom Convolutional Neural Network are also used. Three classes of histopathological lung tissue images: lung squamous cell carcinoma, lung adenocarcinoma, and normal tissue. Images are scaled to 224*224 pixels and preprocessed using data augmentation and normalisation techniques to enhance generalisation before training. With a test accuracy of 96.62%, the suggested Custom CNN is comparable to existing deep learning models like DenseNet121 (96.6%) and other CNN-based methods documented in earlier research. The suggested approach uses Grad-CAM to provide visual interpretability alongside classification performance.