ABSTRACT
“NeuroMedAssist: An Intelligent Brain Tumor Classification System using Deep Learning” is a pioneering Research that leverages advanced deep learning models, specifically VGG16 and Xception, to create an intelligent system for accurate brain tumor classification. The system addresses the limitations of traditional methods by employing cutting-edge neural network structures trained on diverse brain tumor datasets. The Research encompasses a user-friendly web interface for seamless interaction. The comparative analysis between the VGG16 and Xception models reveals their distinct classification performances, aiding in selecting the most suitable model. The proposed system demonstrates exceptional precision, recall, and F1-score across multiple tumor classes, showcasing its robustness. NeuroMedAssist excels in accurate tumor identification and provides preventive information based on the predicted class. The Research holistic approach, encompassing model comparison, user interface, and performance evaluation, positions NeuroMedAssist as an invaluable tool in the field of medical diagnostics, offering a significant advancement in intelligent brain tumor classification systems.
