The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
Brain Tumor Detection by Using Stacked Autoencoders in Deep Learning
Abstract
Brain tumor detection depicts a tough job because of its shape, size and appearance variations. In this manuscript, a deep learning model is deployed to predict input slices as a tumor (unhealthy)/non-tumor (healthy). This manuscript employs a high pass filter image to prominent the inhomogeneities field effect of the MR slices and fused with the input slices. Moreover, the median filter is applied to the fused slices. The resultant slices quality is improved with smoothen and highlighted edges of the input slices. After that, based on these slices' intensity, a 4-connected seed growing algorithm is applied, where optimal threshold clusters the similar pixels from the input slices. The segmented slices are then supplied to the fine-tuned two layers proposed stacked sparse autoencoder (SSAE) model. The hyperparameters of the model are selected after extensive experiments. At the first layer, 200 hidden units and at the second layer 400 hidden units are utilized. The testing is performed on the softmax layer for the prediction of the images having tumors and no tumors. The suggested model is trained and checked on BRATS datasets i.e., 2012(challenge and synthetic), 2013, and 2013 Leaderboard, 2014, and 2015 datasets. The presented model is evaluated with a number of performance metrics which demonstrates the improved performance.
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“The model was finalized after the analysis of best-performing methods existing in the literature, such as [43,52,53,58,66].”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [53]
Citations
Cites
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MethodInfluentialBrain tumor segmentation with Deep Neural Networks
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Cited by
Sorted by citations
Brain tumor detection and classification using machine learning: a comprehensive survey
Amin, J., Sharif, M. I., Haldorai, A., Yasmin, M., & Nayak, R. S. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00563-y · Cited by 511
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Khan, P., Kader, M. F., Islam, S. M. R., Rahman, A. B., Kamal, M. S., Toha, M. U., & Kwak, K. S. (2021) · IEEE Access · doi:10.1109/access.2021.3062484 · Cited by 314
Role of deep learning in brain tumor detection and classification (2015 to 2020): A review
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Automated Brain Tumor Segmentation and Classification in MRI Using YOLO-Based Deep Learning
Almufareh, M. F., Imran, M., Khan, A., Humayun, M., & Asim, M. F. (2024) · IEEE Access · doi:10.1109/access.2024.3359418 · Cited by 218
Metadata
- Authors
- Amin, J., Sharif, M., Gul, N., Raza, M., Anjum, M. A., Nisar, M. W., & Bukhari, S. A. C.
- Affiliations
- COMSATS University Islamabad (PK)
- Wah Medical College (PK)
- National University of Sciences and Technology (PK)
- St. John's University (US)
- Corresponding author
- Sharif, Muhammad
- Venue
- Journal of Medical Systems
- Published
- 2019-12-17
- Volume
- 44
- Issue
- 2
- Pages
- 32-32
- ISSN
- 0148-5598 (ISSN-L)
- 1573-689X
- OpenAlex
- W2996632711
- Cited by
- 178
- References
- 61
- Citations by year
- 2026: 5 · 2025: 22 · 2024: 40 · 2023: 36 · 2022: 44 · 2021: 24 · 2020: 6 · 2019: 1
- FWCI
- 8.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Medical Systems (publishedVersion)
- PubMed (publishedVersion)
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- brain tumor detection, stacked sparse autoencoder, MRI segmentation, image fusion, high-pass filter, median filter, BRATS dataset, tumor classification, medical image analysis
- MeSH terms
- Deep Learning, Algorithms, Brain Neoplasms, Diagnosis, Computer-Assisted, Humans, Image Processing, Computer-Assisted
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