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Application of deep transfer learning for automated brain abnormality classification using MR images

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)176-188
Number of pages13
JournalCognitive Systems Research
DateAccepted/In press - 13 Dec 2018
DatePublished (current) - 1 May 2019


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.

    Research areas

  • Abnormal brain images, CNN, Deep transfer learning, MRI classification


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