Brain Tumor Detection: Deploying Stochastic Gradient Descent Classifier in a Web App

The early detection of brain tumors is crucial for effective treatment and patient outcomes. In this paper, we propose a novel approach for brain tumor detection by deploying a Stochastic Gradient Descent (SGD) Classifier within a user-friendly web application. Our method aims to provide accessible and efficient brain tumor detection capabilities to both clinicians and patients. By leveraging machine learning technology and web-based infrastructure, our system enables users to upload medical images, preprocess them, and obtain predictions for the presence of brain tumors. Through a detailed analysis of the development process, including data preprocessing, model training, web app integration, and deployment, we demonstrate the feasibility and effectiveness of our approach. The system's performance is evaluated through experiments on a test dataset, showcasing high accuracy and efficiency in brain tumor detection. By comparing our system with existing approaches, we highlight its potential to contribute significantly to the field of medical imaging and healthcare. The deployment of our web application facilitates widespread access to brain tumor detection capabilities, potentially leading to improved early diagnosis and treatment of brain-related diseases. Overall, our research presents a promising solution for enhancing brain tumor detection through the seamless integration of machine learning and web technologies.