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Convolutional neural networks for multi-class brain disease detection using MRI images

Research output: Contribution to journalArticle

Original languageEnglish
Article number101673
JournalComputerized Medical Imaging and Graphics
Volume78
Early online date10 Oct 2019
DOIs
DateAccepted/In press - 4 Oct 2019
DateE-pub ahead of print - 10 Oct 2019
DatePublished (current) - 1 Dec 2019

Abstract

The brain disorders may cause loss of some critical functions such as thinking, speech, and movement. So, the early detection of brain diseases may help to get the timely best treatment. One of the conventional methods used to diagnose these disorders is the magnetic resonance imaging (MRI) technique. Manual diagnosis of brain abnormalities is time-consuming and difficult to perceive the minute changes in the MRI images, especially in the early stages of abnormalities. Proper selection of the features and classifiers to obtain the highest performance is a challenging task. Hence, deep learning models have been widely used for medical image analysis over the past few years. In this study, we have employed the AlexNet, Vgg-16, ResNet-18, ResNet-34, and ResNet-50 pre-trained models to automatically classify MR images in to normal, cerebrovascular, neoplastic, degenerative, and inflammatory diseases classes. We have also compared their classification performance with pre-trained models, which are the state-of-art architectures. We have obtained the best classification accuracy of 95.23% ± 0.6 with the ResNet-50 model among the five pre-trained models. Our model is ready to be tested with huge MRI images of brain abnormalities. The outcome of the model will also help the clinicians to validate their findings after manual reading of the MRI images.

    Research areas

  • Brain disease, CNN, Deep transfer learning, MRI classification, ResNet

Documents

Documents

  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Elsevier at https://www.sciencedirect.com/science/article/pii/S0895611119300886?via%3Dihub. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 1.7 MB, PDF document

    Embargo ends: 10/10/20

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    Licence: CC BY-NC-ND

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