Overview of Deep Learning Algorithms and Optimizers for Brain Tumor Segmentation
Brain tumor segmentation is a vital process in medical imaging, essential for accurate diagnosis, treatment planning, and monitoring of brain tumors. Over the years, segmentation techniques have evolved from manual methods to machine learning approaches and, more recently, to deep learning technique...
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2025-04-01
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| Series: | Journal of Medical Physics |
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| Online Access: | https://journals.lww.com/10.4103/jmp.jmp_12_25 |
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| author | Nisha Purohit Chandi Prasad Bhatt |
| author_facet | Nisha Purohit Chandi Prasad Bhatt |
| author_sort | Nisha Purohit |
| collection | DOAJ |
| description | Brain tumor segmentation is a vital process in medical imaging, essential for accurate diagnosis, treatment planning, and monitoring of brain tumors. Over the years, segmentation techniques have evolved from manual methods to machine learning approaches and, more recently, to deep learning techniques. The advent of deep learning, particularly convolutional neural networks, has revolutionized the field, allowing for end-to-end learning and eliminating the need for manual feature extraction. This review focuses on analyzing different deep learning architectures and explores their performance when optimized using different optimizers. While deep learning techniques have significantly improved segmentation accuracy and robustness, challenges remain, particularly in terms of computational complexity, dataset imbalance, and generalization across diverse clinical settings. Achieved high segmentation accuracy in brain tumor detection, outperforming traditional methods with improved Dice scores, precision, and computational efficiency. Different machine learning and deep learning-based architectures and optimized models yielded superior performance, with Dice scores up to 0.91 and validation accuracy of 98%. Achieved dice scores of 0.84, 0.85, and 0.91 for enhancing tumor, tumor core, and whole tumor, respectively, with a mean intersection over union of 0.8665. Future research directions include exploring transfer learning, improving dataset diversity, and developing explainable artificial intelligence techniques to enhance clinical adoption. The insights from this review emphasize the need for further research to overcome existing limitations and expand the applicability of deep learning models in brain tumor segmentation. |
| format | Article |
| id | doaj-art-1df41f08e830480ca4e49b87fc818e63 |
| institution | DOAJ |
| issn | 0971-6203 1998-3913 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Journal of Medical Physics |
| spelling | doaj-art-1df41f08e830480ca4e49b87fc818e632025-08-20T02:57:34ZengWolters Kluwer Medknow PublicationsJournal of Medical Physics0971-62031998-39132025-04-0150218519710.4103/jmp.jmp_12_25Overview of Deep Learning Algorithms and Optimizers for Brain Tumor SegmentationNisha PurohitChandi Prasad BhattBrain tumor segmentation is a vital process in medical imaging, essential for accurate diagnosis, treatment planning, and monitoring of brain tumors. Over the years, segmentation techniques have evolved from manual methods to machine learning approaches and, more recently, to deep learning techniques. The advent of deep learning, particularly convolutional neural networks, has revolutionized the field, allowing for end-to-end learning and eliminating the need for manual feature extraction. This review focuses on analyzing different deep learning architectures and explores their performance when optimized using different optimizers. While deep learning techniques have significantly improved segmentation accuracy and robustness, challenges remain, particularly in terms of computational complexity, dataset imbalance, and generalization across diverse clinical settings. Achieved high segmentation accuracy in brain tumor detection, outperforming traditional methods with improved Dice scores, precision, and computational efficiency. Different machine learning and deep learning-based architectures and optimized models yielded superior performance, with Dice scores up to 0.91 and validation accuracy of 98%. Achieved dice scores of 0.84, 0.85, and 0.91 for enhancing tumor, tumor core, and whole tumor, respectively, with a mean intersection over union of 0.8665. Future research directions include exploring transfer learning, improving dataset diversity, and developing explainable artificial intelligence techniques to enhance clinical adoption. The insights from this review emphasize the need for further research to overcome existing limitations and expand the applicability of deep learning models in brain tumor segmentation.https://journals.lww.com/10.4103/jmp.jmp_12_25brain tumor segmentationdeep learningmedical imagingoptimizer |
| spellingShingle | Nisha Purohit Chandi Prasad Bhatt Overview of Deep Learning Algorithms and Optimizers for Brain Tumor Segmentation Journal of Medical Physics brain tumor segmentation deep learning medical imaging optimizer |
| title | Overview of Deep Learning Algorithms and Optimizers for Brain Tumor Segmentation |
| title_full | Overview of Deep Learning Algorithms and Optimizers for Brain Tumor Segmentation |
| title_fullStr | Overview of Deep Learning Algorithms and Optimizers for Brain Tumor Segmentation |
| title_full_unstemmed | Overview of Deep Learning Algorithms and Optimizers for Brain Tumor Segmentation |
| title_short | Overview of Deep Learning Algorithms and Optimizers for Brain Tumor Segmentation |
| title_sort | overview of deep learning algorithms and optimizers for brain tumor segmentation |
| topic | brain tumor segmentation deep learning medical imaging optimizer |
| url | https://journals.lww.com/10.4103/jmp.jmp_12_25 |
| work_keys_str_mv | AT nishapurohit overviewofdeeplearningalgorithmsandoptimizersforbraintumorsegmentation AT chandiprasadbhatt overviewofdeeplearningalgorithmsandoptimizersforbraintumorsegmentation |