BRAIN TUMOR DETECTION FROM MRI IMAGES USING MACHINE AND DEEP LEARNING TECHNIQUES: A REVIEW
Shruti Suryavanshi*, Khushi Shekokar, Karishma More, Varsha Patil, Satish Bramhan, Pritam Patil
ABSTRACT
Detection of brain tumor is a difficult task that entails identifying malignant tissues from different and diffuse brain medical imaging. This is a crucial stage in computer-aided diagnostic (CAD) systems, as cancerous areas must be identified for viewing and analysis. Image segmentation and classification of brain tumors have to be automated. The principle of this work is to provide an overview of the Magnetic Resonance Imaging (MRI)-based approach for brain tumors detection. Deep learning-based techniques, which automatically create multilevel and separated from unprocessed data, have made significant progress in brain tumor detection recently. These techniques outperformed traditional machine learning techniques that employed handmade characteristics to explain the distinctions between sick and healthy tissues. We provide a complete summary of modern advances in deep learning-based approaches for brain tumor detection (BTD) from MRI in this study. Furthermore, we address the most typical issues and provide potential remedies.
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