Deep Learning-Based Disk Herniation Computer Aided Diagnosis System From MRI Axial Scans

Abstract

Computer-aided diagnosis (CAD) systems have been the focus of many researchers in both computer and medical fields. In this paper, we build two convolutional neural network (CNN) based CAD systems for diagnosing lumbar disk herniation from Magnetic Resonance Imaging (MRI) axial scans. The first one is a disk herniation detection CAD system which is a binary CAD system that determines whether the case image contains disk herniation or not. The second system is a disk herniation type classification CAD system which can determine the type of the disk herniation in the image if one exists. To train and test the proposed systems, an image set is built and annotated with the help of a radiologist. In order to get rid of the 'noisy' parts of the input images and reduce their complexity, we experiment with different ROI extraction methods. The image set is also preprocessed and enlarged using augmentation techniques to make it suitable to be used with CNN. There are many novel aspects of this work. First, the problems of disk herniation detection and recognition from axial scans are not well-studied in the literature. Second, we use deep learning techniques which produces ground-breaking results in many image processing tasks, but are yet to reach their full potential with medical image processing in general. Finally, we explore the use of many innovative techniques such as data augmentation and transfer learning, which greatly improves the accuracy of our models. The results of our systems are impressive with a 95.65% accuracy for the detection problem and a 91.38% accuracy for the recognition problem.