A Machine Learning and Deep Learning based webapp used to predict multiple diseases.
-
Updated
Dec 9, 2022 - Jupyter Notebook
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: https://doi.org/10.32614/cran.package.epocakir
chronic kidney disease detection using different neural network technique
A collection of scripts for filtering annotated variant call format files
Kidney Disease Prediction using Machine Learning
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.
A simple chatbot to respond any queries regarding Kidney diseases.
Genetic Epidemiology of Kidney Disease in African Populations
Interactive web UI and enhanced analytics platform for the Computational Patient (Barbiero & Liò, 2020). Enhanced Edition by Prof. Dr. Utku Köse (2026).
3D kidney pathology, WebAR
Kidney-Genetics - database of kidney-related genes
Quantitative layer based analysis for renal magnetic resonance imaging.
ALY6980: Capstone Project – Chronic Kidney Disease (CKD) Diagnosis Using Machine Learning
Deep Learning-based classification of diabetic kidney disease from histopathology images using CNN models and medical image analysis techniques.
🫘🫁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.
To associate your repository with the kidney-disease topic, visit your repo's landing page and select "manage topics."