The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Application of deep transfer learning for automated brain abnormality classification using MR images
Abstract
Abstract Magnetic resonance imaging (MRI) is the most common imaging technique used to detect abnormal brain tumors. Traditionally, MRI images are analyzed manually by radiologists to detect the abnormal conditions in the brain. Manual interpretation of huge volume of images is time consuming and difficult. Hence, computer-based detection helps in accurate and fast diagnosis. In this study, we proposed an approach that uses deep transfer learning to automatically classify normal and abnormal brain MR images. Convolutional neural network (CNN) based ResNet34 model is used as a deep learning model. We have used current deep learning techniques such as data augmentation, optimal learning rate finder and fine-tuning to train the model. The proposed model achieved 5-fold classification accuracy of 100% on 613 MR images. Our developed system is ready to test on huge database and can assist the radiologists in their daily screening of MR images.
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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
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
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
Cited by
A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor
Noreen, N., Palaniappan, S., Qayyum, A., Ahmad, I., Imran, M., & Shoaib, M. (2020) · IEEE Access · doi:10.1109/access.2020.2978629 · Cited by 478
Deep CNN for Brain Tumor Classification
Ayadi, W., Elhamzi, W., Charfi, I., & Atri, M. (2021) · Neural Processing Letters · doi:10.1007/s11063-020-10398-2 · Cited by 448
Transfer learning techniques for medical image analysis: A review
Kora, P., Ooi, C. P., Faust, O., Raghavendra, U., Gudigar, A., Chan, W. Y., Meenakshi, K., Swaraja, K., Plawiak, P., & Rajendra Acharya, U. (2022) · Biocybernetics and Biomedical Engineering · doi:10.1016/j.bbe.2021.11.004 · Cited by 367
Self-citationTiwari, A., Srivastava, S., & Pant, M. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.11.020 · Cited by 361
A survey on deep learning in medicine: Why, how and when?
Piccialli, F., Somma, V. D., Giampaolo, F., Cuomo, S., & Fortino, G. (2021) · Information Fusion · doi:10.1016/j.inffus.2020.09.006 · Cited by 348
Metadata
- Authors
- Talo, M., Baloglu, U. B., Yıldırım, Ö., & Rajendra Acharya, U.
- Venue
- Cognitive Systems Research
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 54
- Pages
- 176-188
- ISSN
- 1389-0417 (ISSN-L)
- OpenAlex
- W2906302663
- Cited by
- 507
- References
- 3
- FWCI
- 19.86
- Type
- journal article
- Language
- en
- Open access
- closed
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- Talo, M., Baloglu, U. B., Yıldırım, Ö., & Rajendra Acharya, U. (2019). Application of deep transfer learning for automated brain abnormality classification using MR images. Cognitive Systems Research. https://doi.org/10.1016/j.cogsys.2018.12.007
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