Abstract
Brain tumors are one of the most severe medical conditions that require immediate attention and treatment. The early detection of brain tumors is of utmost importance, as it can significantly improve the chances of successful treatment outcomes and increase the patient's quality of life. This study proposes a novel methodology for the early detection of brain tumors in magnetic resonance imaging (MRI) images using a modified VGG16 neural network architecture. The dataset comprises both tumor and non-tumor MRI images collected from Kaggle and has preprocessing techniques applied to optimize the model's performance. The proposed approach delivers an impressive accuracy rate of 99.08%, demonstrating its efficacy in precise brain tumor detection. This new methodology is expected to aid doctors in accurate diagnosis and treatment planning, thereby helping to save more lives and improve the quality of life of patients suffering from brain tumors.
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