Abstract
This study intuitively addresses a crucial need in the medical domain by introducing a customized Mobile-Net model for classifying brain cancer in medical imaging data. The paramount importance of accurate classification in cancer diagnosis and treatment planning cannot be emphasized further. This particular research primarily focuses on the practical application of semantic classification techniques to precisely identify and outline brain cancer zones in medical imaging data. By utilizing a Mobile-Net architecture, the developed model highlights outstanding performance with an accuracy score of 85%.
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