AI-Powered Alzheimer's Detection
Collaborative academic web application for AI-assisted MRI and symptom-text classification.
Academic project · Team of four · FSTE Errachidia, Université Moulay Ismaïl · 2024–2025
In this case study
Overview
Developed collaboratively by a team of four, this web application supports AI-assisted Alzheimer’s screening through two complementary models: a CNN for MRI image classification and an SVM for symptom-text classification. The models are integrated into an ASP.NET Core MVC application with a responsive interface for image uploads, symptom analysis, and displaying prediction results.
Results
- CNN for MRI image classification: 85.2% reported model accuracy.
- SVM for symptom-text classification: 96% reported model accuracy.
- Both models integrated into a responsive ASP.NET Core MVC application for uploads, analysis, and prediction results.
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Key contributions
- AI model training and evaluation.
- Web application development.
- Model and API integration.
- Collaborative software development.
Key features
- MRI image upload and CNN classification
- Symptom-text analysis with TF-IDF and SVM
- Prediction results and reports
- Model integration through a REST API
- Responsive ASP.NET Core MVC interface