Abstract
In recent years, as chemotherapy has advanced, it is important to accurately diagnosis the histological type (adenocarcinoma, squamous cell carcinoma and small cell carcinoma). Pathologists diagnose not only images but also the patientʼs clinical background. In this study, we aimed to develop automated classification scheme of lung cancer type by combining liquid-based cytological (LBC) images and electronic medical record. First, image features were extracted from LBC images using deep convolutional neural network (DCNN). Subsequently, patient clinical data (smoking status etc.) were collected, and dimension compression was performed by principal component analysis (PCA). Image features and patient clinical data corresponding to cytological images were given to the classifier. Finally, classification result of 3 histological categories was obtained. In the experiments, the proposed method was applied to 149 cases (Adenocarcinoma; 50, Squamous cell carcinoma; 51, Small cell carcinoma; 48) and evaluated via 3-fold cross-validation. As a result of experiments, the classification accuracy of the cytological image alone using DCNN was 82.9%. When the image feature and patient basic data (age, gender, smoking status etc.), or image feature and tumor markers were given to the support vector machine (SVM), the classification accuracy was improved. These results indicate that the proposed method may be useful for histological classification of lung cancer.
