  Lung and colon cancer are serious worldwide health challenges that require
early and precise identification to reduce mortality risks. However, diagnosis,
which is mostly dependent on histopathologists' competence, presents
difficulties and hazards when expertise is insufficient. While diagnostic
methods like imaging and blood markers contribute to early detection,
histopathology remains the gold standard, although time-consuming and
vulnerable to inter-observer mistakes. Limited access to high-end technology
further limits patients' ability to receive immediate medical care and
diagnosis. Recent advances in deep learning have generated interest in its
application to medical imaging analysis, specifically the use of
histopathological images to diagnose lung and colon cancer. The goal of this
investigation is to use and adapt existing pre-trained CNN-based models, such
as Xception, DenseNet201, ResNet101, InceptionV3, DenseNet121, DenseNet169,
ResNet152, and InceptionResNetV2, to enhance classification through better
augmentation strategies. The results show tremendous progress, with all eight
models reaching impressive accuracy ranging from 97% to 99%. Furthermore,
attention visualization techniques such as GradCAM, GradCAM++, ScoreCAM, Faster
Score-CAM, and LayerCAM, as well as Vanilla Saliency and SmoothGrad, are used
to provide insights into the models' classification decisions, thereby
improving interpretability and understanding of malignant and benign image
classification.
