Detection of Medical Image Findings and Discrimination between Benign and Malignant Nodules in Chest CT Images by Using Deep Learning

Abstract

Computer-aided diagnosis (CADx) systems based on deep learning have been actively researched in recent years, and it has been reported to exhibit their high performance. The CADx systems for benign and malignant discrimination exhibit a similar tendency. They generally convert input medical images into likelihood of their benignancy and malignancy. On the other hand, when medical doctors explain the diagnosis to patients, they need to explain not only the likelihood of malignancy of the lung nodule but also basis of the diagnosis. In this paper, we proposed a CADx system which provides medical doctor with likelihood of the existence of the medical image finding related to lung cancer CNN (convolutional neural network) for obtaining the likelihood of the medical image findings, and NN (neural network) for obtaining the likelihood of the malignancy of the lung nodule. As a result of evaluating the performance of the proposed system using 55 benign and 120 malignant nodules, the discrimination rate was 79.02±8.43 [%].