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In contrast to conventional methods, the proposed methodology incorporates an advanced image enhancement algorithm. This algorithm involves a multi-step preprocessing approach, including applying a Kuwahara filter, a median filter, and a homomorphic sharpening filter. The primary purpose is to enhance the visibility of tumor structures in MRI images, effectively highlighting relevant features while mitigating unwanted details and noise. This study adopts a comparative analysis strategy, independently employing VGG16, ResNet18, and DenseNet pretrained models to classify brain tumors. By evaluating and contrasting the performance of these models, we aim to identify the most adequate one in terms of accuracy and efficiency. Our findings demonstrate that implementing this methodology with deep learning architectures results in an outstanding classification accuracy of up to 95%. This research brings significant progress to automated brain tumor classification by providing detailed insights into the strengths and limitations of various deep-learning models used in medical imaging. The results serve as a practical guide for future research, which could lead to the development of more accurate diagnostic tools. Specifically, the findings can help create targeted tools for diagnosing glioma, meningioma, no tumor, and pituitary classes, which could bring advancements in medical imaging technology, leading to more efficient neurology diagnostics. |
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