The CLP Publishing collects CLP journals and partnered optics and photonics journals, and provides readers an optical publishing platform with global influence. SPIE, OSA, CUP. Chinese Laser Press (CLP), established by Shanghai Institute of Optics and Fine Mechanics (SIOM), Chinese Academy of Sciences (CAS) and Chinese Optical Society (COS) in 2009, nowadays publishes eight journals and manages three online platforms.

Lung cancer (LC) is one of the most serious cancers threatening human health. Histopathological examination is the gold standard for qualitative and clinical staging of lung tumors. However, the process for doctors to examine thousands of histopathological images is very cumbersome, especially for doctors with less experience. Therefore, objective pathological diagnosis results can effectively help doctors choose the most appropriate treatment mode, thereby improving the survival rate of patients. For the current problem of incomplete experimental subjects in the computer-aided diagnosis of lung cancer subtypes, this study included relatively rare lung adenosquamous carcinoma (ASC) samples for the first time, and proposed a computer-aided diagnosis method based on histopathological images of ASC, lung squamous cell carcinoma (LUSC) and small cell lung carcinoma (SCLC). Firstly, the multidimensional features of 121 LC histopathological images were extracted, and then the relevant features (Relief) algorithm was used for feature selection. The support vector machines (SVMs) classifier was used to classify LC subtypes, and the receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to make it more intuitive evaluate the generalization ability of the classifier. Finally, through a horizontal comparison with a variety of mainstream classification models, experiments show that the classification effect achieved by the Relief-SVM model is the best. The LUSC-ASC classification accuracy was 73.91%, the LUSC-SCLC classification accuracy was 83.91% and the ASC-SCLC classification accuracy was 73.67%. Our experimental results verify the potential of the auxiliary diagnosis model constructed by machine learning (ML) in the diagnosis of LC.