Transfer‐learning‐based classification of pathological brain magnetic resonance images

Different diseases occur in the brain. For instance, hereditary and progressive diseases affect and degenerate the white matter. Although addressing, diagnosing, and treating complex abnormalities in the brain is challenging, different strategies have been presented with significant advances in medi...

Full description

Saved in:
Bibliographic Details
Published in:ETRI journal 2024, 46(2), , pp.263-276
Main Authors: Savaş, Serkan, Damar, Çağrı
Format: Article
Language:English
Subjects:
brain MRI
deep learning
EfficientNet
transfer learning
two-tier cross-test
white matter
전자/정보통신공학
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Different diseases occur in the brain. For instance, hereditary and progressive diseases affect and degenerate the white matter. Although addressing, diagnosing, and treating complex abnormalities in the brain is challenging, different strategies have been presented with significant advances in medical research. With state‐of‐art developments in artificial intelligence, new techniques are being applied to brain magnetic resonance images. Deep learning has been recently used for the segmentation and classification of brain images. In this study, we classified normal and pathological brain images using pretrained deep models through transfer learning. The EfficientNet‐B5 model reached the highest accuracy of 98.39% on real data, 91.96% on augmented data, and 100% on pathological data. To verify the reliability of the model, fivefold cross‐validation and a two‐tier cross‐test were applied. The results suggest that the proposed method performs reasonably on the classification of brain magnetic resonance images.
ISSN:1225-6463
2233-7326
DOI:10.4218/etrij.2022-0088