Liu Mingyang, Li Liyuan, Wang Haoran, Guo Xinyu, Liu Yunpeng, Li Yuguang, Song Kaiwen, Shao Yanbin, Wu Fei, Zhang Junjie, Sun Nao, Zhang Tianyu, Luan Lan
Key Laboratory of Geophysical Exploration Equipment, Ministry of Education, College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China.
Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, China.
Front Oncol. 2023 May 18;13:1172234. doi: 10.3389/fonc.2023.1172234. eCollection 2023.
Lung 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.
This 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.
The 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.
In summary, MIM has high classifification performance and substantial potential in lung cancer detection tasks.
肺癌是人类最常见的恶性肿瘤之一。肺腺癌是另一种最常见的肺癌类型。在临床医学中，医生依靠病理检查提供的信息作为许多疾病最终诊断的重要参考。因此，病理诊断被称为疾病诊断的金标准。然而，病理图像中包含的信息复杂性以及患者数量的增加远远超过了病理学家的数量，尤其是在欠发达国家的肺癌治疗中。
本文提出了一种用于肺癌组织病理学图像检测的多层感知器模型，该模型能够自动检测肺腺癌的浸润程度。针对肺癌组织病理学图像中存在的大量局部信息，多层感知器中的多层感知器（MLP IN MLP，MIM）采用双数据流输入方法，实现了一种结合全局和局部信息的建模方法，以提高模型的分类性能。在我们的实验中，我们收集了780张肺癌组织病理学图像，并准备了一个肺组织病理学图像数据集来验证MIM的有效性。
MIM的诊断准确率达到95.31%，其精确率、灵敏度、特异度和F1分数分别为95.31%、93.09%、93.10%、96.43%和93.10%，优于普通网络模型的诊断结果。此外，一系列扩展实验证明了MIM的可扩展性和稳定性。
综上所述，MIM在肺癌检测任务中具有较高的分类性能和巨大的潜力。
