A multilayer perceptron-based model applied to histopathology image classification of lung adenocarcinoma subtypes

Frontiers in Oncology

Oncology

Nao Sun¹  Lan Luan²  Fei Wu²  Junjie Zhang²  Yunpeng Liu³  Yanbin Shao⁴  Haoran Wang⁴  Yuguang Li⁴  Liyuan Li⁴  Tianyu Zhang⁴  Kaiwen Song⁴  Mingyang Liu⁴  Xinyu Guo⁴ 

[1] Center for Reproductive Medicine and Center for Prenatal Diagnosis, The First Hospital of Jilin University, Changchun, China;Department of Pathology, Central Hospital Affiliated to Shenyang Medical College, Shenyang, China;Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, China;Key Laboratory of Geophysical Exploration Equipment, Ministry of Education, College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China;

关键词: histopathology image;    lung cancer;    deep learning;    multilayer perceptron;    image classification;   

DOI  :  10.3389/fonc.2023.1172234

 received in 2023-03-02, accepted in 2023-05-05,  发布年份 2023

来源: Frontiers

【 摘 要 】

ObjectiveLung cancer is one of the most common malignant tumors in humans. Adenocarcinoma of the lung is another of the most common types of lung cancer. In clinical medicine, physicians rely on the information provided by pathology tests as an important reference for the fifinal 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. MethodsThis paper proposes a multilayer perceptron model for lung cancer histopathology image detection, which enables the automatic detection of the degree of lung adenocarcinoma infifiltration. 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 classifification 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. ResultsThe MIM achieves a diagnostic accuracy of 95.31% and has a precision, sensitivity, specificity and F1-score of 95.31%, 93.09%, 93.10%, 96.43% and 93.10% respectively, outperforming the diagnostic results of the common network model. In addition, a number of series of extension experiments demonstrated the scalability and stability of the MIM.ConclusionsIn summary, MIM has high classifification performance and substantial potential in lung cancer detection tasks.

【 授权许可】

Unknown   
Copyright © 2023 Liu, Li, Wang, Guo, Liu, Li, Song, Shao, Wu, Zhang, Sun, Zhang and Luan