Brain tumor detection using fusion of hand crafted and deep learning features
Abstract
Abstract The perilous disease in the worldwide now a days is brain tumor. Tumor affects the brain by damaging healthy tissues or intensifying intra cranial pressure. Hence, rapid growth in tumor cells may lead to death. Therefore, early brain tumor diagnosis is a more momentous task that can save patient from adverse effects. In the proposed work, the Grab cut method is applied for accurate segmentation of actual lesion symptoms while Transfer learning model visual geometry group (VGG-19) is fine-tuned to acquire the features which are then concatenated with hand crafted (shape and texture) features through serial based method. These features are optimized through entropy for accurate and fast classification and fused vector is supplied to classifiers. The presented model is tested on top medical image computing and computer-assisted intervention (MICCAI) challenge databases including multimodal brain tumor segmentation (BRATS) 2015, 2016, and 2017 respectively. The testing results with dice similarity coefficient (DSC) achieve 0.99 on BRATS 2015, 1.00 on BRATS 2015 and 0.99 on BRATS 2017 respectively.
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The model for brain tumor detection was finalized after analysis of best-performing methods existing in the literature.
“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 [52]
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Naeem, A., Anees, T., Naqvi, R. A., & Loh, W.-K. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020275 · Cited by 103 · see citing works
- Authors
- Saba, T., Sameh Mohamed, A., El-Affendi, M., Amin, J., & Sharif, M.
- Affiliations
- Prince Sultan University (SA)
- COMSATS University Islamabad (PK)
- Corresponding author
- Sharif, Muhammad
- Venue
- Cognitive Systems Research
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 59
- Pages
- 221-230
- ISSN
- 1389-0417 (ISSN-L)
- OpenAlex
- W2974878728
- Semantic Scholar
- 3fd5027b3f844f173c3423aae275abfad5c494ca
- Cited by
- 433
- References
- 46
- Citations by year
- 2026: 20 · 2025: 60 · 2024: 91 · 2023: 94 · 2022: 84 · 2021: 63 · 2020: 18 · 2019: 3
- FWCI
- 18.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cognitive Systems Research (publishedVersion)
- Funders
- Prince Sultan University
- Topics
- Life Sciences › Neuroscience › Neurology › 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, brain tumor segmentation, feature fusion, handcrafted features, shape features, texture features, VGG19, transfer learning, GrabCut, entropy-based feature selection, BraTS, medical image classification
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