This repository houses machine learning models and pipelines for predicting various diseases, coupled with an integration with a Large Language Model for Diet and Food Recommendation. Each disease prediction task has its dedicated directory structure to maintain organization and modularity.

[ECCV 2024] Official Implementation of 《WSI-VQA: Interpreting Whole Slide Image by Generative Question Answering》

This is a repo for the Tanzania AI lab hackathon 2020 & the AI4Dev2020 challenge, where we as the Elixir team created the 1st AI based cancer diagnosis system, built a model comprising of Deep Convolutional Neural Network(CNN) and a web app that screens microscopic images so as to detect cancer tumors, thus increasing speed, accuracy in cancer d…

Parkinson detection based on wave sketches and deep learning - PDS

Rule-based healthcare expert system designed using Pyke and Python. The project focuses on heart failure telemonitoring, aiming to enhance patient self-care and clinical management.

RadioCare: Fighting Inefficiencies in Medical Imaging

SG Healthcare AI Datathon 2021 - acute kidney injury (AKI) patients requiring replacement renal therapy

A machine learning-based web app for detecting Parkinson's disease from voice recordings. The app extracts key voice features, applies pre-trained models, and provides real-time predictions of Parkinson's likelihood. Built using Streamlit, Librosa, and scikit-learn.

Predicting wether your all ok or not based off of data

This repository shows how we can develop and save ML and DL model and use them to predict and diagnosis different diseases.

Hackathon notes

Example of generated automatic target dossier with an LLM

This repository includes all notes of AI in Healthcare Specialization offered by the Standford University.

Heart and Lung Sounds Dataset Recorded from a Clinical Manikin using Digital Stethoscope (HLS-CMDS)

This project uses a TinyVGG16-based CNN to classify MRI scans for Alzheimer's Disease stages: Mild Impairment, Moderate Impairment, No Impairment, and Very Mild Impairment. It includes Jupyter notebooks for training and prediction, and a Streamlit app for easy inference. The model achieves high metrics in predicting Alzheimer's stages.

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