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This repository is related to the thesis paper titled as "ALzheimer's Disease & Dementia Detection From 3D Brain MRI Data Using Deep Convolutional Neural Networks." This thesis paper was accepted and published by IEEE's 3rd INTERNATIONAL CONFERENCE FOR CONVERGENCE IN TECHNOLOGY ( I2CT), PUNE, INDIA - 6-8 APRIL, 2018.
🏆 HackPrinceton 1st Place (Health Hack + Best Use Machine Learning) - Developed method for Alzheimer’s and Dementia early on-set detection. 💊 Built AWS Lambda backend, NLP classifiers, & integrated software with Amazon Alexa
A Machine Learning Approach for Cognitive Decline Detection Using Neuroimaging Data Developed a multi-model system to predict brain tumor, dementia, and schizophrenia diseases. Integrated these models into a Flask app for real-time predictions.
An application that allows users to predict the risk of a patient having dementia based from their MRI Scan and other medical data using Three Ensemble machine learning methods
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
Machine learning model for predicting dementia risk using non-medical factors only. Includes data cleaning, feature selection, PCA, and classification models (RF, XGBoost, SVM) with explainability.
Binary classification system for dementia risk prediction using non-medical features. ModelX Optimization Sprint submission by IEEE Society, IIT Sri Lanka. Achieves 0.9254 ROC-AUC using only demographic and lifestyle data from NACC dataset
Cognify is a cloud-based AI system developed as a University Final Year Project. It leverages deep learning techniques to analyse MRI images and detect signs of Alzheimer’s Disease (AD)