Advanced diagnostic decision-support system for Alzheimer’s detection using a Voting-Based Ensemble ML model and Cognitive Feature Analysis.

Built a simple AI that looks at brain MRI scans and tells you if there’s no Alzheimer’s, very mild, mild, or moderate signs; right now it gets it right 89% of the time . It runs on EfficientNet-B0, trains fast in Colab, and a streamlit web app where you can upload any MRI and get an instant answer. Everything is open-source

Deep learning system for Alzheimer's disease detection from brain MRI scans using transfer learning with pre-trained CNNs (MobileNet, VGG, InceptionV3), classifies into 3 stages (AD/CI/CN)

Deep learning–based Alzheimer’s disease classification using OCTA image feature extraction.

This project is part of the submission for the Intermediate Data Scientist Learning Path on the Dicoding platform. It is also a part of the IDCAMP 2024 training program.

Boosting Handwriting-Based Alzheimer's Detection through Late Fusion with Speech — MSc Thesis @ UniPA 2026

Transformer-based re-implementation of INTERSPEECH 2021 Alzheimer's speech detection — MSc research @ UniPA

Deep learning tool for Alzheimer’s stage classification from MRI scans using custom CNN (98% accuracy), saliency maps, confidence visualization, and Streamlit interface with PDF reports.

This repository contains a comprehensive deep learning solution for Alzheimer's Disease Classification using state-of-the-art DenseNet architectures optimized with Optuna hyperparameter tuning. The project implements multiple DenseNet variants for classification of Alzheimer's disease stages from brain MRI images.

Conference-accepted research project on early detection of Alzheimer’s disease using self-supervised learning, multimodal fusion, and domain harmonisation for cross-site robustness.

This project was developed as part of the PIDEV – 2nd Year Engineering Program at Esprit School of Engineering (Academic Year 2025–2026). Built with Angular, Spring Boot, and microservices architecture.