A Machine Learning and Deep Learning based webapp used to predict multiple diseases.

Medical Diagnosis A Machine Learning Based Web Application

A rule-based algorithm enabled the automatic extraction of disease labels from tens of thousands of radiology reports. These weak labels were used to create deep learning models to classify multiple diseases for three different organ systems in body CT.

Using Supervised Machine Learning Techniques for Chronic Kidney Disease Detection

🏥 Clinical coding of patients with kidney disease using KDIGO clinical practice guidelines

chronic kidney disease detection using different neural network technique

A collection of scripts for filtering annotated variant call format files

Machine learning algorithm is used to detect whether the person will suffer from chronic kidney disease or not.

This webapp predict the whether the person have diabetes,heart disease,liver disease,kidney disease , back pain,tuberculosis.

3D kidney pathology, WebAR

A simple chatbot to respond any queries regarding Kidney diseases.

Kidney-Genetics - database of kidney-related genes

A web-based application that predicts multiple diseases including diabetes, heart disease, stroke, chronic kidney disease, and liver disease using machine learning models. The system leverages optimized ML algorithms to provide accurate predictions and offers a user-friendly interface for input and result visualization.

ALY6980: Capstone Project – Chronic Kidney Disease (CKD) Diagnosis Using Machine Learning

🫘🫁NephroScan is an AI-powered kidney stone detection system that uses deep learning models ResNet50, MobileNetV3, and DenseNet201 to analyze kidneys for the presence of stones. The backend server processes medical images and returns detection results via a REST API, seamlessly integrated into a mobile application for real-time diagnostics.

Quantitative layer based analysis for renal magnetic resonance imaging.

Codes for my project called " Fatty Liver Classification and Scaling: from 0 to 2 using CNN." In this project, I've created my own neural network and trained it with the images of the kidneys with the fat level scaling from 0 to 2. With each given new data to the network, the programme indicates which level of fatness the liver is categorized.

Dectecting chronic heart disease using machine learning 100%

A pipeline for filtering annotated variant call format files