MRANet: A Modified Residual Attention Networks for Lung and Colon Cancer Classification

Lung and colon cancers are the most common causes of cancer deaths. For treatment to work, it is very important to find out what is wrong as soon as possible and correctly. Learning models that use imaging technology to find different types of images have shown promise in automating the process of classifying cancer from histopathological images. The histopathological diagnosis is a big part of figuring out what kind of cancer someone has. This research aims to create a deep-learning model that can find lung and colon cancer in histopathological images with a high degree of accuracy. We suggested a new way to do things that is based on an enhanced version of the residual attention network structure. The model got its training from 25,000 high-resolution histopathological images that were split into groups. Our proposed model was achieved very high accuracy with scores of 99.30% for two classes, 96.63% for three classes, and 97.56% for five classes, respectively. These numbers are better compared to other modern architectures. This study shows that a deep learning model can accurately determine the difference between lung and colon cancer. Our proposed model’s better performance meets a key need in medical AI applications.