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Custom CNN model improves brain tumor classification accuracy, indicating effective tumor location identification in MRIs.
Brain Tumors are contributing significantly to the global mortality rates. These brain tumors must be detected early and should be classified accurately. Magnetic Resonance Imaging (MRI) is the most commonly used imaging technique. It plays a key role in accurately classifying brain tumors. MRI provides high soft tissue contrast and gives information about tumor location, size and shape. This study proposes an MRI technique for brain tumor classification. In this study, we have developed a customized convolutional neural network (CNN) model for predicting brain tumors. The customized CNN model that classifies the type of tumor. The customized CNN is compared to other existing models to derive our conclusion. The model’s efficiency is evaluated using accuracy, precision, F1 score, and recall.
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Smiley et al. (2026) studied this question.
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