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.

翻译：肺癌与结肠癌作为严重的全球性健康挑战，亟需通过早期精准识别以降低死亡风险。然而，主要依赖组织病理学家专业水平的诊断方法在经验不足时存在困难与风险。尽管影像学检查和血液标志物等诊断手段有助于早期发现，组织病理学仍被视为金标准，但其耗时且易受观察者间差异影响。高端技术普及不足进一步限制了患者及时获得诊疗服务的能力。深度学习的最新进展激发了其在医学影像分析领域的应用潜力，特别是利用组织病理图像诊断肺癌与结肠癌的研究。本研究旨在应用并改进现有的基于预训练CNN的模型（包括Xception、DenseNet201、ResNet101、InceptionV3、DenseNet121、DenseNet169、ResNet152和InceptionResNetV2），通过优化数据增强策略提升分类性能。结果显示所有八种模型均取得显著进展，准确率高达97%至99%。此外，采用GradCAM、GradCAM++、ScoreCAM、Faster Score-CAM、LayerCAM以及Vanilla Saliency和SmoothGrad等注意力可视化技术，为模型分类决策提供解释依据，从而增强对恶性与良性图像分类的可解读性。