Article
Improved Brain Tumor Detection and Classification in Brain MRI using Deep Learning Techniques
57-73Since early detection may strongly support ap- propriate treatment decisions, brain tumor analysis utilizing magnetic resonance imaging (MRI) is a crucial duty in medical diagnosis. The demand for automated solutions arises due to the time-consuming nature of manual MRI scan evaluation and its heavy reliance on expert interpretation. This study proposes a computeraided system for brain tumor analysis that integrates a deep learning-based classification method with fuzzy thresholding-based picture segmentation. MRI brain pictures are processed initially to improve clarity and minimize undesired noise. Fuzzy thresholding, which aids in controlling intensity changes found in medical images, is then used to segregate tumor areas. Following segmentation, the pictures are classified into appropriate tumor classifications using a deep learning model. When compared to conventional methods, the experimental results show that the suggested structure offers dependable and consistent performance. Overall, the established method provides an effective and automated way to help medical practitioners identify and categorize brain tumors
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