MULTIPLE BRAIN DISEASE PREDICTION WITH DIAGNOSTIC REPORT USING DEEP LEARNING

Abstract

The prevalence of brain diseases such as Alzheimer's disease, brain tumor, and strokes poses significant challenges to public health worldwide. Early detection and accurate diagnosis of these conditions are crucial for timely intervention and improved patient outcomes. In this context, the development of a deep learning-based system for brain disease prediction represents a promising approach to address these challenges. This project aims to create a web application that utilizes a Convolutional Neural Network (CNN) algorithm to predict the three main brain diseases affecting humans: Alzheimer's disease, brain tumor and stroke. Users can upload MRI or CT scan images of their brain to the web application, which then analyzes the images using the trained CNN model to determine whether the individual is affected by any of the targeted brain diseases. If a brain disease is detected, the application generates a detailed diagnostic report, specifying the type of disease and providing relevant information such as disease severity. Users have the option to view the report within the application interface and download it in A4 sheet format for future reference. In future, Extending the system to predict a broader range of brain diseases beyond the initial four main diseases. This expansion would require additional training data and fine-tuning of the deep learning model to recognize a diverse set of disease patterns. Exploring opportunities for personalized medicine by integrating clinical data with medical imaging data to tailor treatment plans to individual patients' needs and genetic profiles. Key Words: Brain Disease, Deep Learning Model, CNN, Alzheimer's disease, brain tumor, stroke, Accurate Diagnosis