Next-Generation Automation in Neuro-Oncology: Advanced Neural Networks for MRI-Based Brain Tumor Segmentation and Classification
Brain tumors provide a significant healthcare concern worldwide due to their potentially lethal consequences and the intricate nature of their diagnosis. These tumors exhibit significant variability in kind, size, and location, hence confounding identification and treatment efforts. Timely and preci...
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IEEE
2025-01-01
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| author | Syed Sajid Hussain Niyaz Ahmad Wani Jasleen Kaur Naveed Ahmad Sadique Ahmad |
| author_facet | Syed Sajid Hussain Niyaz Ahmad Wani Jasleen Kaur Naveed Ahmad Sadique Ahmad |
| author_sort | Syed Sajid Hussain |
| collection | DOAJ |
| description | Brain tumors provide a significant healthcare concern worldwide due to their potentially lethal consequences and the intricate nature of their diagnosis. These tumors exhibit significant variability in kind, size, and location, hence confounding identification and treatment efforts. Timely and precise detection is essential, as it profoundly influences treatment efficacy and survival probabilities. Contemporary diagnostic techniques, predominantly reliant on Magnetic Resonance Imaging (MRI), necessitate considerable manual analysis by experts, resulting in possible delays and inconsistencies in diagnosis. In response to the urgency of these difficulties, our research presents a novel multi-task learning methodology utilizing advanced neural network architectures to automate and improve the accuracy of brain tumor identification and classification from MRIs. This method seeks to optimize the diagnostic procedure, diminish reliance on manual analysis, and deliver swift, dependable outcomes that can expedite the commencement of treatment. The efficacy of our methodology is evidenced by comprehensive testing of three sophisticated neural architectures: UNet, Attention-UNet, and Residual-Attention-UNet. Our results indicate that the Residual-Attention-UNet model significantly surpasses the others in segmentation accuracy and classification precision. Our studies, utilizing conventional metrics including the Jaccard Similarity Index (JSI), Dice Coefficients (DC), and overall Accuracy (ACC), demonstrated that the Residual-Attention-UNet attained roughly 89.30% JSI, 91.10% DC, and 93.35% ACC. In binary classification tasks, this model achieved a Precision of 98.60%, Recall of 98.06%, Accuracy of 99.40%, and an F1 score of 96.57%. Furthermore, in multiclass classification contexts, the model consistently above 95% across all measures, underscoring its robustness and the efficacy of our proposed multi-task learning approach. These results highlight the capability of our method to substantially progress the domain of medical imaging for brain tumors, providing a robust instrument for improving diagnostic precision and patient management in neuro-oncology. |
| format | Article |
| id | doaj-art-63e99314f42f4f7d9f6db92f2e51f0c6 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-63e99314f42f4f7d9f6db92f2e51f0c62025-08-20T02:47:29ZengIEEEIEEE Access2169-35362025-01-0113411414115810.1109/ACCESS.2025.354779610909528Next-Generation Automation in Neuro-Oncology: Advanced Neural Networks for MRI-Based Brain Tumor Segmentation and ClassificationSyed Sajid Hussain0https://orcid.org/0000-0003-3376-2290Niyaz Ahmad Wani1https://orcid.org/0000-0002-7656-3374Jasleen Kaur2https://orcid.org/0000-0003-0051-5832Naveed Ahmad3https://orcid.org/0000-0003-2941-9780Sadique Ahmad4https://orcid.org/0000-0001-6907-2318School of Computer Science, UPES, Dehradun, IndiaSchool of Computer Science and Engineering, IILM University, Greater Noida, Uttar Pradesh, IndiaDepartment of Computer Science and Engineering, Thapar Institute of Engineering & Technology, Patiala, Punjab, IndiaCollege of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi ArabiaEIAS Data Science and Blockchain Laboratory, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi ArabiaBrain tumors provide a significant healthcare concern worldwide due to their potentially lethal consequences and the intricate nature of their diagnosis. These tumors exhibit significant variability in kind, size, and location, hence confounding identification and treatment efforts. Timely and precise detection is essential, as it profoundly influences treatment efficacy and survival probabilities. Contemporary diagnostic techniques, predominantly reliant on Magnetic Resonance Imaging (MRI), necessitate considerable manual analysis by experts, resulting in possible delays and inconsistencies in diagnosis. In response to the urgency of these difficulties, our research presents a novel multi-task learning methodology utilizing advanced neural network architectures to automate and improve the accuracy of brain tumor identification and classification from MRIs. This method seeks to optimize the diagnostic procedure, diminish reliance on manual analysis, and deliver swift, dependable outcomes that can expedite the commencement of treatment. The efficacy of our methodology is evidenced by comprehensive testing of three sophisticated neural architectures: UNet, Attention-UNet, and Residual-Attention-UNet. Our results indicate that the Residual-Attention-UNet model significantly surpasses the others in segmentation accuracy and classification precision. Our studies, utilizing conventional metrics including the Jaccard Similarity Index (JSI), Dice Coefficients (DC), and overall Accuracy (ACC), demonstrated that the Residual-Attention-UNet attained roughly 89.30% JSI, 91.10% DC, and 93.35% ACC. In binary classification tasks, this model achieved a Precision of 98.60%, Recall of 98.06%, Accuracy of 99.40%, and an F1 score of 96.57%. Furthermore, in multiclass classification contexts, the model consistently above 95% across all measures, underscoring its robustness and the efficacy of our proposed multi-task learning approach. These results highlight the capability of our method to substantially progress the domain of medical imaging for brain tumors, providing a robust instrument for improving diagnostic precision and patient management in neuro-oncology.https://ieeexplore.ieee.org/document/10909528/Brain tumorclassificationdeep learningMRIsegmentation |
| spellingShingle | Syed Sajid Hussain Niyaz Ahmad Wani Jasleen Kaur Naveed Ahmad Sadique Ahmad Next-Generation Automation in Neuro-Oncology: Advanced Neural Networks for MRI-Based Brain Tumor Segmentation and Classification IEEE Access Brain tumor classification deep learning MRI segmentation |
| title | Next-Generation Automation in Neuro-Oncology: Advanced Neural Networks for MRI-Based Brain Tumor Segmentation and Classification |
| title_full | Next-Generation Automation in Neuro-Oncology: Advanced Neural Networks for MRI-Based Brain Tumor Segmentation and Classification |
| title_fullStr | Next-Generation Automation in Neuro-Oncology: Advanced Neural Networks for MRI-Based Brain Tumor Segmentation and Classification |
| title_full_unstemmed | Next-Generation Automation in Neuro-Oncology: Advanced Neural Networks for MRI-Based Brain Tumor Segmentation and Classification |
| title_short | Next-Generation Automation in Neuro-Oncology: Advanced Neural Networks for MRI-Based Brain Tumor Segmentation and Classification |
| title_sort | next generation automation in neuro oncology advanced neural networks for mri based brain tumor segmentation and classification |
| topic | Brain tumor classification deep learning MRI segmentation |
| url | https://ieeexplore.ieee.org/document/10909528/ |
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