Brain Tumor Detection and Classification Model Using Deep Learning and Medical Imaging Techniques

Abstract

Brain tumors present one of the most critical challenges in medical diagnostics due to their complex nature and high mortality rates. Early and accurate detection is essential for effective treatment planning and improving patient outcomes. This study proposes an autonomous deep learning framework for brain tumor detection and classification using advanced medical imaging techniques. A comprehensive dataset of annotated brain MRI images was used to train and evaluate several deep learning architectures, including CNN, ResNet, and VGGNet. The fine-tuned CNN model demonstrated superior performance with an accuracy of 94.6% in tumor classification and segmentation tasks. The framework integrates a user-friendly interface for clinicians, enabling seamless visualization of scan results and suggested classifications. The proposed system has the potential to improve diagnostic accuracy, reduce cognitive load on medical professionals, and support radiologists in making timely and accurate clinical decisions.

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