Deep Learning Advancement for Brain-Tumor Classification and Comprehensive Diagnosis

To effectively treat brain tumors and enhance patient outcomes, early and precise tumor diagnosis is essential. This research proposes a complete methodology for classifying brain tumors from MRI images using Convolutional Neural Networks (CNNs), more specifically the VGG16 architecture. Preprocessing MRI scans to improve and standardize data quality is part of the methodology, and the VGG16 model is then used for analysis. With a high accuracy of 98.33 %, this model classifies tumors into gliomas, meningiomas, pituitary tumors, and nontumor situations. Furthermore, a web-based platform that combines this detection algorithm with a multilingual chatbot (Telugu, Tamil, and English) has been established. It offers users comprehensive details on symptoms, available treatments, and medical consultations. An important development in automated brain tumor diagnosis and patient support has been made with the combination of this sophisticated model with an intuitive user interface. In addition to increasing diagnostic precision, this system makes important data easier to obtain and provides a comprehensive approach to early detection and patient involvement.