The DOI matches its Crossref record; 60 of the 65 references it cites were found in a registry or an index.
Convolutional neural networks for multi-class brain disease detection using MRI images
Open accessAbstract
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.
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“Although MRI and CT image segmentation research includes different organs such as knee cartilage, prostate, and liver, most research work has concentrated on brain segmentation, particularly tumors [295–300].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [297]
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Automated detection of COVID-19 cases using deep neural networks with X-ray images
Öztürk, T., Talo, M., Yildirim, E. A., Baloğlu, U. B., Yıldırım, Ö., & Acharya, U. R. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103792 · Cited by 2,652
Self-citationTransfer learning for medical image classification: a literature review
Kim, H. E., Cosa‐Linan, A., Santhanam, N., Jannesari, M., Maros, M. E., & Ganslandt, T. (2022) · BMC Medical Imaging · doi:10.1186/s12880-022-00793-7 · Cited by 1,117
A scoping review of transfer learning research on medical image analysis using ImageNet
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A review of deep learning with special emphasis on architectures, applications and recent trends
Sengupta, S., Basak, S., Saikia, P., Paul, S., Tsalavoutis, V. A., Atiah, F. D., Ravi, V., & Peters, A. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105596 · Cited by 490
Metadata
- Authors
- Talo, M., Yildirim, O., Baloglu, U. B., Aydin, G., & Acharya, U. R.
- Affiliations
- Munzur University (TR)
- International Research Organization for Advanced Science and Technology (JP)
- Venue
- Computerized Medical Imaging and Graphics
- Publisher
- Elsevier BV
- Published
- 2019-12
- Volume
- 78
- Pages
- 101673
- ISSN
- 0730-4862 (ISSN-L)
- 0895-6111
- OpenAlex
- W2979487364
- Cited by
- 301
- References
- 65
- Citations by year
- 2026: 8 · 2025: 32 · 2024: 47 · 2023: 60 · 2022: 63 · 2021: 61 · 2020: 27 · 2019: 2
- FWCI
- 16.84
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Computerized Medical Imaging and Graphics (publishedVersion)
- PubMed (publishedVersion)
- DSpace - FIRAT (Fırat University) (submittedVersion, cc-by-nc-nd)
- Bristol Research (University of Bristol) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Keywords
- brain disease detection, MRI classification, convolutional neural networks, pre-trained models, ResNet50, AlexNet, VGG16, cerebrovascular disease, brain tumors, degenerative brain disease, neuroinflammation, automated medical diagnosis
- MeSH terms
- Deep Learning, Brain Diseases, Humans, Magnetic Resonance Imaging, Neural Networks, Computer, Early Diagnosis, Datasets as Topic
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