AN INTELLIGENT NEURO-ONCOLOGY HEALTHCARE FRAMEWORK FOR BRAIN TUMOR IDENTIFICATION AND MRI-BASED REGION ANALYSIS
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Department of Computer Science and Engineering,
Malla Reddy College of Engineering and Technology (MRCET), Hyderabad, Telangana, India.
Malla Reddy College of Engineering and Technology (MRCET), Hyderabad, Telangana, India.
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Abstract
Brain tumors pose significant challenges in early diagnosis and treatment planning due to variations in their appearance, size, and location. Magnetic Resonance Imaging (MRI) provides valuable anatomical information for identifying and analyzing brain tumors. This research proposes an intelligent neuro-oncology framework using deep learning for brain tumor classification and region analysis. A multiresolution feature extraction mechanism captures local texture patterns and global anatomical features, while attention-based feature fusion improves classification performance. Additionally, U-Net enables tumor-region segmentation when annotated data are available. Grad-CAM provides visual explanations, and performance is evaluated using accuracy, precision, recall, F1-score, Dice coefficient, and Intersection over Union.
Keywords: Brain Tumor Detection, Magnetic Resonance Imaging (MRI), Deep Learning, Multiresolution Feature Extraction.
Keywords: Brain Tumor Detection, Magnetic Resonance Imaging (MRI), Deep Learning, Multiresolution Feature Extraction.
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Double-Blind Peer Review
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CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 9
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Sep 25 2026
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Ishma, S. (2026). An Intelligent Neuro-Oncology Healthcare Framework for Brain Tumor Identification and MRI-Based Region Analysis. International Journal of Science, Strategic Management and Technology, 02(9), 1-9. https://doi.org/10.55041/ijsmt.v2i9.010
Ishma, Shaik. "An Intelligent Neuro-Oncology Healthcare Framework for Brain Tumor Identification and MRI-Based Region Analysis." International Journal of Science, Strategic Management and Technology, vol. 02, no. 9, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijsmt.v2i9.010.
Ishma, Shaik. "An Intelligent Neuro-Oncology Healthcare Framework for Brain Tumor Identification and MRI-Based Region Analysis." International Journal of Science, Strategic Management and Technology 02, no. 9 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijsmt.v2i9.010.
References
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[3] S. Deepa, J. Janet, S. Sumathi, and J. P. Ananth, “Hybrid Optimization Algorithm Enabled Deep Learning Approach Brain Tumor Segmentation and Classification Using MRI,” Journal of Digital Imaging, vol. 36, no. 3, pp. 847–868, 2023, doi: 10.1007/s10278-022-00752-2. PubMed
[4] S. Srinivasan, P. S. M. Bai, S. K. Mathivanan, V. Muthukumaran, J. C. Babu, and L. Vilcekova, “Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique,” Diagnostics, vol. 13, no. 6, Art. no. 1153, 2023, doi: 10.3390/diagnostics13061153. PubMed
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[8] A. Akter et al., “Robust Clinical Applicable CNN and U-Net Based Algorithm for MRI Classification and Segmentation for Brain Tumor,” Expert Systems with Applications, vol. 238, Part F, Art. no. 122347, 2024, doi: 10.1016/j.eswa.2023.122347. ScienceDirect
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[10] “Deep Learning Approaches for Brain Tumor Detection and Classification Using MRI Images (2020 to 2024): A Systematic Review,” Journal of Digital Imaging, vol. 38, no. 3, pp. 1403–1433, 2025, doi: 10.1007/s10278-024-01283-8. PMC
[2] N. Abdel Samee, T. Ahmad, N. F. Mahmoud, G. Atteia, H. A. Abdallah, and A. Rizwan, “Clinical Decision Support Framework for Segmentation and Classification of Brain Tumor MRIs Using a U-Net and DCNN Cascaded Learning Algorithm,” Healthcare, vol. 10, no. 12, Art. no. 2340, 2022, doi: 10.3390/healthcare10122340. PubMed
[3] S. Deepa, J. Janet, S. Sumathi, and J. P. Ananth, “Hybrid Optimization Algorithm Enabled Deep Learning Approach Brain Tumor Segmentation and Classification Using MRI,” Journal of Digital Imaging, vol. 36, no. 3, pp. 847–868, 2023, doi: 10.1007/s10278-022-00752-2. PubMed
[4] S. Srinivasan, P. S. M. Bai, S. K. Mathivanan, V. Muthukumaran, J. C. Babu, and L. Vilcekova, “Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique,” Diagnostics, vol. 13, no. 6, Art. no. 1153, 2023, doi: 10.3390/diagnostics13061153. PubMed
[5] “Efficient Simultaneous Segmentation and Classification of Brain Tumors from MRI Scans Using Deep Learning,” Biocybernetics and Biomedical Engineering, vol. 43, no. 3, pp. 616–633, 2023, doi: 10.1016/j.bbe.2023.08.003. ScienceDirect
[6] M. K. H. Khan et al., “Machine Learning and Deep Learning for Brain Tumor MRI Image Segmentation,” Experimental Biology and Medicine, vol. 248, no. 21, 2023, doi: 10.1177/15353702231214259. Sage Journals
[7] E. Cekic, E. Pinar, M. Pinar, and A. Dagcinar, “Deep Learning-Assisted Segmentation and Classification of Brain Tumor Types on Magnetic Resonance and Surgical Microscope Images,” World Neurosurgery, vol. 182, pp. e196–e204, 2024, doi: 10.1016/j.wneu.2023.11.073.ScienceDirect
[8] A. Akter et al., “Robust Clinical Applicable CNN and U-Net Based Algorithm for MRI Classification and Segmentation for Brain Tumor,” Expert Systems with Applications, vol. 238, Part F, Art. no. 122347, 2024, doi: 10.1016/j.eswa.2023.122347. ScienceDirect
[9] I. Gupta, S. Singh, S. Gupta, and S. R. Nayak, “Classification of Brain Tumours in MRI Images Using a Convolutional Neural Network,” Current Medical Imaging, vol. 20, 2024, Art. no. e270323214998,doi:10.2174/1573405620666230327124902. PubMed
[10] “Deep Learning Approaches for Brain Tumor Detection and Classification Using MRI Images (2020 to 2024): A Systematic Review,” Journal of Digital Imaging, vol. 38, no. 3, pp. 1403–1433, 2025, doi: 10.1007/s10278-024-01283-8. PMC
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