A multilayer perceptron-based model applied to histopathology image classification of lung adenocarcinoma subtypes - Liu, Mingyang; Li, Liyuan; Wang, Haoran; Guo, Xinyu; Liu, Yunpeng; Li, Yuguang; Song, Kaiwen; Shao, Yanbin; Wu, Fei; Zhang, Junjie; Sun, Nao; Zhang, Tianyu; et al. (Frontiers in Oncology, 2023)
Lung cancer is one of the most common malignant tumors in humans. It has a very high lethality rate because its early symptoms are not obvious. In clinical medicine, physicians rely on the information provided by pathology tests as an important reference for the final diagnosis of many diseases. Thus, pathological diagnosis is known as the gold standard for disease diagnosis. However, the complexity of the information contained in pathology images and the increase in the number of patients far exceeds the number of pathologists, especially in the treatment of lung cancer in less-developed countries. Therefore, this paper proposes a multilayer perceptron model for lung cancer histopathology image detection, which enables the automatic detection of the degree of lung adenocarcinoma infiltration. For the large amount of local information present in lung cancer histopathology images, MLP IN MLP (MIM) uses a dual data stream input method to achieve a modeling approach that combines global and local information to improve the classification performance of the model. In our experiments, we collected 780 lung cancer histopathological images and prepared a lung histopathology image dataset to verify the effectiveness of MIM. The diagnostic accuracy of MIM reached 95.31%, which is better than that of common network models. In addition some series of extension experiments proved the scalability and stability of MIM. In summary, MIM has high classification performance and substantial potential in lung cancer detection tasks.