A COMPUTER-AIDED SUPPORT SYSTEM FOR DIAGNOSIS OF CT IMAGES BY MACHINE LEARNINGS

Abstract

Interstitial pneumonia causes abnormal iflammations of lung tissues and those are represented in CT images. Therefore, state of the disease is judged from the CT images manually by medical specialists and computer-aided diagnosis is expected. In this paper, we develop an image processing system with machine learning techniques in order to support the diagnosis. At first, we construct an image processing to extract lung regions from CT images by using genetic programming techniques. Then, an index is proposed to identify patients of interstitial pneumonia from information of the extracted lung regions. Finally, each CT image is classified into 6 patterns according to diseased area in the lung regions with the aid of convolutional neural network. Consequently, the network can classify the CT images with more than 90% accuracy.