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 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.