Abstract
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.
Abstract (translated)
肺癌和结直肠癌是全球性的健康挑战，需要早期和精确的识别以降低死亡率风险。然而，病理学家依赖的诊断方法在专业知识不足时会带来困难和风险。虽然影像技术和血液标记物等诊断方法有助于早期诊断，但组织学仍然是金标准，尽管时间漫长且易受操作者错误的影响。限制高端技术的访问程度进一步限制了患者获得及时医疗护理和诊断的能力。近年来在深度学习方面的进步引起了对其在医学影像分析中的应用的关注，特别是使用组织学图像诊断肺癌和结直肠癌。本研究的目标是利用和适应现有的预训练CNN模型，如Xception、DenseNet201、ResNet101、InceptionV3、DenseNet121、DenseNet169、ResNet152和InceptionResNetV2，通过更好的增强策略来增强分类。结果显示，所有模型都取得了巨大的进步，所有八个模型都达到了令人印象深刻的准确率，从97%到99%不等。此外，还使用了注意力图技术，如GradCAM、GradCAM++、ScoreCAM、Faster Score-CAM和LayerCAM，以及Vanilla Saliency和SmoothGrad，以提供模型分类决策的洞察，从而改善恶性和良性图像分类的可解释性和理解。
URL
https://arxiv.org/abs/2405.04610
