The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
An enhanced deep learning approach for brain cancer MRI images classification using residual networks
Abstract
Cancer is the second leading cause of death after cardiovascular diseases. Out of all types of cancer, brain cancer has the lowest survival rate. Brain tumors can have different types depending on their shape, texture, and location. Proper diagnosis of the tumor type enables the doctor to make the correct treatment choice and help save the patient's life. There is a high need in the Artificial Intelligence field for a Computer Assisted Diagnosis (CAD) system to assist doctors and radiologists with the diagnosis and classification of tumors. Over recent years, deep learning has shown an optimistic performance in computer vision systems. In this paper, we propose an enhanced approach for classifying brain tumor types using Residual Networks. We evaluate the proposed model on a benchmark dataset containing 3064 MRI images of 3 brain tumor types (Meningiomas, Gliomas, and Pituitary tumors). We have achieved the highest accuracy of 99% outperforming the other previous work on the same dataset.
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Cites
The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary
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A survey on Image Data Augmentation for Deep Learning
Shorten, C., & Khoshgoftaar, T. M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0197-0 · Cited by 13,203
(2016) · Cited by 11,679
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
Identity Mappings in Deep Residual Networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · arXiv · doi:10.48550/arxiv.1603.05027 · Cited by 10,665
The Balanced Accuracy and Its Posterior Distribution
Brodersen, K. H., Ong, C. S., Stephan, K. E., & Buhmann, J. M. (2010) · 2010 20th International Conference on Pattern Recognition · doi:10.1109/icpr.2010.764 · Cited by 1,816
Data augmentation for improving deep learning in image classification problem
Mikolajczyk, A., & Grochowski, M. (2018) · 2018 International Interdisciplinary PhD Workshop (IIPhDW) · doi:10.1109/iiphdw.2018.8388338 · Cited by 1,697
An Introduction to Convolutional Neural Networks
O’Shea, K., & Nash, R. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.08458 · Cited by 951
Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition
Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., Wang, Z., & Feng, Q. (2015) · PLOS ONE · doi:10.1371/journal.pone.0140381 · Cited by 879
Brain Tumor Classification Using Convolutional Neural Network
Abiwinanda, N., Hanif, M., Hesaputra, S. T., Handayani, A., & Mengko, T. R. (2019) · IFMBE Proceedings · doi:10.1007/978-981-10-9035-6_33 · Cited by 631
Cited by
Data Augmentation in Classification and Segmentation: A Survey and New Strategies
Alomar, K., Aysel, H. I., & Cai, X. (2023) · Journal of Imaging · doi:10.3390/jimaging9020046 · Cited by 319
Metadata
- Authors
- Abdelaziz Ismael, S. A., Mohammed, A., & Hefny, H.
- Venue
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 102
- Pages
- 101779
- ISSN
- 0933-3657 (ISSN-L)
- OpenAlex
- W2995378448
- Cited by
- 417
- References
- 36
- FWCI
- 15.08
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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