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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
(2014) · Cited by 33,419
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273
Neurophysiological investigation of the basis of the fMRI signal
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001) · Nature · doi:10.1038/35084005 · Cited by 6,551
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2538465 · Cited by 2,735
Marcus, D. S., Wang, T. H., Parker, J., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2007) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2007.19.9.1498 · Cited by 2,163
An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058
Deep learning for brain MRI segmentation: state of the art and future directions
Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L., & Erickson, B. J. (2017) · Journal of Digital Imaging · doi:10.1007/s10278-017-9983-4 · Cited by 1,129
Classification using deep learning neural networks for brain tumors
Mohsen, H., El‐Dahshan, E. A., El-Horbaty, E.-S. M., & Salem, A.-B. M. (2017) · Future Computing and Informatics Journal · doi:10.1016/j.fcij.2017.12.001 · Cited by 1,076
An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2018) · NORA - Norwegian Open Research Archives · Cited by 1,012
Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions
Agarwal, A., Negahban, S., & Wainwright, M. J. (2012) · The Annals of Statistics · doi:10.1214/12-aos1000 · Cited by 654
Medical Imaging 2015: Computer-Aided Diagnosis
Hadjiiski, L. M., & Tourassi, G. D. (2015) · SPIE eBooks · Cited by 601
Hybrid intelligent techniques for MRI brain images classification
El‐Dahshan, E. A., Hosny, T., & Salem, A.-B. M. (2009) · Digital Signal Processing · doi:10.1016/j.dsp.2009.07.002 · Cited by 576
Chaplot, S., Patnaik, L. M., & Jagannathan, N. R. (2006) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2006.05.002 · Cited by 539
Maier, O., Menze, B. H., von der Gablentz, J., Häni, L., Heinrich, M. P., Liebrand, M., Winzeck, S., Basit, A., Bentley, P., Chen, L., et al. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.07.009 · Cited by 528
Application of deep transfer learning for automated brain abnormality classification using MR images
Talo, M., Baloğlu, U. B., Yıldırım, Ö., & Acharya, U. R. (2018) · Cognitive Systems Research · doi:10.1016/j.cogsys.2018.12.007 · Cited by 526
Self-citationMcBee, M. P., Awan, O. A. R., Colucci, A. T., Ghobadi, C. W., Kadom, N., Kansagra, A. P., Tridandapani, S., & Auffermann, W. F. (2018) · Academic Radiology · doi:10.1016/j.acra.2018.02.018 · Cited by 512
Ketkar, N. S. (2017) · Apress eBooks · doi:10.1007/978-1-4842-2766-4_12 · Cited by 497
Jain, R., Jain, N., Aggarwal, A., & Hemanth, D. J. (2019) · Cognitive Systems Research · doi:10.1016/j.cogsys.2018.12.015 · Cited by 470
Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review
Bernal, J., Kushibar, K., Asfaw, D. S., Valverde, S., Oliver, A., Martí, R., & Lladó, X. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.08.008 · Cited by 458
Pathological brain detection based on AlexNet and transfer learning
Lu, S., Lu, Z., & Zhang, Y. (2018) · Journal of Computational Science · doi:10.1016/j.jocs.2018.11.008 · Cited by 385
A hybrid method for MRI brain image classification
Zhang, Y., Dong, Z., Wu, L., & Wang, S. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.02.012 · Cited by 360
Longitudinal multiple sclerosis lesion segmentation: Resource and challenge
Carass, A., Roy, S., Jog, A., Cuzzocreo, J. L., Magrath, E., Gherman, A., Button, J., Nguyen, J. V., Prados, F., Sudre, C. H., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.064 · Cited by 357
DeepAD: Alzheimer’s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRIPreprint
Sarraf, S., DeSouza, D. D., Anderson, J. A. E., Tofighi, G., & Initiative, A. D. N. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/070441 · Cited by 341
Günther, J., Pilarski, P. M., Helfrich, G., Shen, H., & Diepold, K. J. (2014) · Procedia Technology · doi:10.1016/j.protcy.2014.09.007 · Cited by 302
Nayak, D. R., Dash, R., & Majhi, B. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.11.034 · Cited by 254
(2014) · Cited by 234
Feed‐forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection
Wang, S., Zhang, Y., Dong, Z., Du, S., Ji, G., Yan, J., Yang, J., Wang, Q., Feng, C.-M., & Phillips, P. (2015) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22132 · Cited by 214
Linked Component Analysis From Matrices to High-Order Tensors: Applications to Biomedical Data
Zhou, G., Zhao, Q., Zhang, Y., Adalı, T., Xie, S., & Cichocki, A. S. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2474704 · Cited by 190
Zhang, Y., Zhang, H., Chen, X., Lee, S., & Shen, D. G. (2017) · Scientific Reports · doi:10.1038/s41598-017-06509-0 · Cited by 154
Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection
Wang, S., Lu, S., Dong, Z., Yang, J., Yang, M., & Zhang, Y. (2016) · Applied Sciences · doi:10.3390/app6060169 · Cited by 137
Strength and similarity guided group-level brain functional network construction for MCI diagnosis
Zhang, Y., Zhang, H., Chen, X., Liu, M., Zhu, X., Lee, S., & Shen, D. G. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.12.001 · Cited by 137
Zhang, Y., Dong, Z., Liu, A., Wang, S., Ji, G., Zhang, Z., & Yang, J. (2015) · Journal of Medical Imaging and Health Informatics · doi:10.1166/jmihi.2015.1542 · Cited by 128
Gudigar, A., Raghavendra, U., Tan, R., Ciaccio, E. J., & Acharya, U. R. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.08.008 · Cited by 110
Self-citationAn Ensemble of 2D Convolutional Neural Networks for Tumor Segmentation
Lyksborg, M., Puonti, O., Agn, M., & Larsen, R. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19665-7_17 · Cited by 104
Moritz, C. H., Haughton, V. M., Cordes, D., Quigley, M., & Meyerand, M. E. (2000) · PubMed Central · Cited by 90
Classification of brain MRI with big data and deep 3D convolutional neural networks
Wegmayr, V., Aitharaju, S., & Buhmann, J. M. (2018) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2293719 · Cited by 54
Nayak, D. R., Dash, R., & Majhi, B. (2016) · CNS & Neurological Disorders - Drug Targets · doi:10.2174/1871527315666161024142036 · Cited by 50
Gudigar, A., Raghavendra, U., Ciaccio, E. J., Arunkumar, N., Abdulhay, E., & Acharya, U. R. (2019) · IEEE Access · doi:10.1109/access.2019.2901055 · Cited by 49
Self-citationDetection of abnormal MR brains based on wavelet entropy and feature selection
Zhou, X., Zhang, Y., Ji, G., Yang, J., Dong, Z., Wang, S., Zhang, G., & Phillips, P. (2016) · IEEJ Transactions on Electrical and Electronic Engineering · doi:10.1002/tee.22226 · Cited by 41
Cheng, L., Zhu, Y., Sun, J., Deng, L., He, N., Yang, Y., Ling, H., Ayaz, H., Fu, Y., & Tong, S. (2018) · International Journal of Neural Systems · doi:10.1142/s0129065718500028 · Cited by 38
Computing, Communication and Signal Processing
(2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-1513-8 · Cited by 38
Pathological brain detection using curvelet features and least squares SVM
Nayak, D. R., Dash, R., & Majhi, B. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-4171-y · Cited by 37
Gilanie, G., Bajwa, U. I., Waraich, M. M., Habib, Z., Ullah, H., & Nasir, M. (2017) · Signal Image and Video Processing · doi:10.1007/s11760-017-1182-8 · Cited by 33
LOCALIZATION OF EPILEPTIC FOCI USING MULTIMODALITY NEUROIMAGING
Olson, L. D., & Perry, M. S. (2012) · International Journal of Neural Systems · doi:10.1142/s012906571230001x · Cited by 29
Pycnonuclear burning of34Ne in accreting neutron stars
Yakovlev, D. G., Gasques, L. R., & Wiescher, M. (2006) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2006.10753.x · Cited by 27
IL‐6 mediates ER expansion during hyperpolarization of alternatively activated macrophages
Ayaub, E. A., Tandon, K., Padwal, M., Imani, J., Patel, H., Dubey, A., Mekhael, O., Upagupta, C., Ayoub, A., Dvorkin-Gheva, A., et al. (2018) · Immunology and Cell Biology · doi:10.1111/imcb.12212 · Cited by 27
Nayak, D. R., Dash, R., Majhi, B., & Acharya, U. R. (2019) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2019.101656 · Cited by 25
Self-citationCombining EEG Microstates with fMRI Structural Features for Modeling Brain Activity
Michalopoulos, K., & Bourbakis, N. (2015) · International Journal of Neural Systems · doi:10.1142/s0129065715500410 · Cited by 24
Beneficence as a principle in human research
Pieper, I. J., & Thomson, C. J. H. (2016) · Bioethics News · doi:10.1007/s40592-016-0061-3 · Cited by 18
Saignavongs, M., Ciumas, C., Petton, M., Bouet, R., Boulogne, S., Rheims, S., Carmichael, D. W., Lachaux, J.-P., & Ryvlin, P. (2016) · International Journal of Neural Systems · doi:10.1142/s0129065717500010 · Cited by 16
Computational intelligence techniques for human brain MRI classification
El‐Dahshan, E. A., & Bassiouni, M. M. (2018) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22265 · Cited by 11
Legaz-Aparicio, Á.-G., Verdú‐Monedero, R., Larrey-Ruiz, J., Morales‐Sánchez, J., López-Mir, F., Naranjo, V., & Bernabéu, Á. (2016) · International Journal of Neural Systems · doi:10.1142/s0129065717500149 · Cited by 8
Hooda, H., & Verma, O. P. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-1513-8_90 · Cited by 3
Flagged first, then by citations
5 of the 65 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906
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
Morid, M. A., Borjali, A., & Del Fiol, G. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104115 · Cited by 508
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
