Application of deep transfer learning for automated brain abnormality classification using MR images |
| |
| Affiliation: | 1. Department of Computer Engineering, Munzur University, Tunceli 62000, Turkey;2. Department of Software Engineering, Firat University, Elazig, Turkey;3. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore;4. Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan;5. International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan;1. School of Information Management & Engineering, ShangHai University of Finance and Economics, 777 Guoding Road, ShangHai City 200433, China;2. Shanghai Key laboratory of Financial Information Technology, 777 Guoding Road, ShangHai City 200433, China;3. Accounting and Information Systems Department, Virginia Tech, 3007 Pamplin Hall, Blacksburg, Virginia 24061, USA |
| |
| 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. |
| |
| Keywords: | MRI classification Abnormal brain images Deep transfer learning CNN |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
