Hybrid Approach for MRI Segmentation using Deep Learning and Machine Learning Algorithms

Abstract

Abstract Accurate MRI segmentation is a crucial part of modern medical diagnostics and is essential for early disease diagnosis and effective treatment planning. Vision Transformers (ViT), Kernel-Based Convolutional Neural Networks (CNN), and Multi-Class Support Vector Machines (M-SVM) are all presented in this study as part of a novel hybrid approach to MRI segmentation that improves accuracy and efficiency.Our method employs ViT, which rapidly extracts high-level features from MRI patches, in combination with kernel-based convolutional neural networks, which are well-known for their ability to capture intricate patterns in image data. The M-SVM then refines the classification process, separating the pixels into distinct classes that are suggestive of different tissue types, and the segmentation phase begins without any problems. In addition to increasing the accuracy of MRI segmentation, initial findings suggest that this novel method might set an innovative standard for the analysis of medical images. This research has the potential to be an important development in medical imaging, which would significantly advance the current state of the art in healthcare technology by improving the accuracy with which diagnoses are made and the effectiveness of treatment plans.