A Comprehensive Review of Brain Tumor Detection Using Machine Learning and Deep Learning: Challenges, Gaps and Future Directions
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Abstract
Patient survival is impacted by the growing prevalence of brain tumor, which makes prompt and precise detection extremely difficult. Machine Learning (ML) and Deep Learning (DL) have become essential for enhancing the identification and categorization of brain tumor due to developments in medical imaging and computational intelligence. This article offers a comprehensive overview of the approaches taken by several researchers to identify brain tumors using ML and DL models. The study points out the shortcomings of the existing approaches, including the need for improved segmentation algorithms, the small dataset size, and the absence of real-time data. In order to fill these gaps, author suggests a hybrid neural network model that reliably localizes tumor and classifies them as benign or malignant by combining Convolutional Neural Networks (CNNs) with sophisticated pre-processing and feature extraction techniques.
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