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

Talo, Muhammed; Yildirim, Ozal; Baloglu, Ulas Baran; Aydin, Galip; Acharya, U Rajendra

doi:10.1016/j.compmedimag.2019.101673

Cited by 304Brain Tumor Detection and ClassificationMedical Image Segmentation TechniquesRetinal Imaging and Analysis

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

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Talo, Muhammed, Yildirim, Ozal, Baloglu, Ulas Baran, Aydin, Galip, Acharya, U Rajendra·2019·Computerized Medical Imaging and Graphics

DOI 10.1016/j.compmedimag.2019.101673

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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.

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Most MRI and CT image segmentation research has concentrated on brain segmentation, particularly tumors.
“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]
The Introduction (page not available) says CNNs have been widely used to segment brain tumors, supporting the claim that brain-tumor segmentation is a substantial research topic. It does not say that most MRI and CT segmentation research focuses on the brain, or compare that work with segmentation of other organs. (LLM, conf=0.84)
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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)
ORCID: Talo, Muhammed
Yildirim, Ozal
Acharya, U Rajendra
Venue: Computerized Medical Imaging and Graphics
Publisher: Elsevier BV
Published: 2019-12
Volume: 78
Pages: 101673
ISSN: 0730-4862 (ISSN-L)
0895-6111
DOI: 10.1016/j.compmedimag.2019.101673
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 · 2012: 1
FWCI: 16.84
Type: journal article
Language: en
Open access: green
hdl.handle.net
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
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: Life Sciences › Neuroscience › Neurology › 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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Convolutional neural networks for multi-class brain disease detection using MRI images

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