Lung and colon cancers are predominant contributors to cancer mortality. Early and accurate diagnosis is crucial for effective treatment. By utilizing imaging technology in different image detection, learning models have shown promise in automating cancer classification from histopathological images. This includes the histopathological diagnosis, an important factor in cancer type identification. This research focuses on creating a high-efficiency deep-learning model for identifying lung and colon cancer from histopathological images. We proposed a novel approach based on a modified residual attention network architecture. The model was trained on a dataset of 25,000 high-resolution histopathological images across several classes. Our proposed model achieved an exceptional accuracy of 99.30%, 96.63%, and 97.56% for two, three, and five classes, respectively; those are outperforming other state-of-the-art architectures. This study presents a highly accurate deep learning model for lung and colon cancer classification. The superior performance of our proposed model addresses a critical need in medical AI applications.

翻译：肺癌和结肠癌是癌症死亡率的主要贡献者。早期且准确的诊断对于有效治疗至关重要。通过在不同图像检测中利用成像技术，学习模型已显示出从组织病理学图像自动进行癌症分类的潜力。这包括组织病理学诊断，这是癌症类型识别中的一个重要因素。本研究专注于创建一个高效深度学习模型，用于从组织病理学图像中识别肺癌和结肠癌。我们提出了一种基于改进残差注意力网络架构的新方法。该模型在包含多个类别的25,000张高分辨率组织病理学图像数据集上进行了训练。我们提出的模型在两个、三个和五个类别上分别取得了99.30%、96.63%和97.56%的卓越准确率；这些结果优于其他最先进的架构。本研究提出了一种用于肺和结肠癌分类的高精度深度学习模型。我们提出模型的优越性能满足了医学人工智能应用中的关键需求。