Application of deep transfer learning for automated brain abnormality classification using MR images. (May 2019)

• Record Type: Journal Article
• Authors: Talo, Muhammed; Baloglu, Ulas Baran; Yıldırım, Özal; Rajendra Acharya, U
• Is Part Of: Cognitive systems research. Volume 54(2019)
• Page Start: 176
• Page End: 188
• Publication Date: 2019-05
• Subjects: MRI classification -- Abnormal brain images -- Deep transfer learning -- CNN; Cognition -- Periodicals; Cognitive engineering (System design) -- Periodicals; Artificial intelligence -- Periodicals; 153.05
• DOI: 10.1016/j.cogsys.2018.12.007
• Languages: English
• ISSNs: 1389-0417

Abstract

Highlights: MR brain images are classified using a pre-trained model and deep transfer learning techniques. The proposed model yields 5-fold classification accuracy of 100% on 613 MR images. The computation cost of the complex layer parameters and the long validation process are eliminated. Automatic classification is provided without any manual feature extraction/selection and pre-processing on the images. Abstract: Magnetic resonance imaging (MRI) is the most common imaging technique used to detect abnormal brain tumors. Traditionally, MRI images are analyzed manually by radiologists to detect the abnormal conditions in the brain. Manual interpretation of huge volume of images is time consuming and difficult. Hence, computer-based detection helps in accurate and fast diagnosis. In this study, we proposed an approach that uses deep transfer learning to automatically classify normal and abnormal brain MR images. Convolutional neural network (CNN) based ResNet34 model is used as a deep learning model. We have used current deep learning techniques such as data augmentation, optimal learning rate finder and fine-tuning to train the model. The proposed model achieved 5-fold classification accuracy of 100% on 613 MR images. Our developed system is ready to test on huge database and can assist the radiologists in their daily screening of MR images.