Mondo Disease Ontology
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Updated
Jun 1, 2024 - Jupyter Notebook
Mondo Disease Ontology
VR-Caps: A Virtual Environment for Active Capsule Endoscopy
Disease classification on different plants with using Machine Learning and Convolutional Neural Networks.
Smart Health Predictor with Data Mining using Php, Mysql. Which can predict the disease based on Input Symptoms and Lab Sample.
Web App for Plant Disease Detection using Tensorflow and streamlit
Multi-Label Image Classification of Chest X-Rays In Pytorch
A menu based multiple chronic disease detection system which will detect if a person is suffering from a severe disease by taking an essential input image.
It's able to detect 33 type of leaf diseases by using Deep learning.. I use transfer learning on the project. For More Information read my code.
Machine Learning in NeuroImaging (MALINI) is a MATLAB-based toolbox used for feature extraction and disease classification using resting state functional magnetic resonance imaging (rs-fMRI) data. 18 different popular classifiers are presented. With slight modifications, it can also be used for any classification problem using any set of features.
Plant Disease Detection model built with Keras and FastAPI
Plants health monitoring through iot and plants disease detection using machine learning in flutter
Machine learning techniques can be used to overcome these drawbacks which are cause due to the high dimensions of the data. So in this project I am using machine learning algorithms to predict the chances of getting cancer.
Disease classification on different plants with using Machine Learning and Convolutional Neural Networks.
diagnose disease faster than ever, well depends on your device I can't help !!!
A Disease-Symptoms Network and a system that predicts diseases from symptoms using a decision tree classifier.
Clustering the medical textbook, in order to categorize diseases based on the most common words in each disease description by using NLP algorithms and techniques.
Deep Learning and AI Enthusiasts to contribute to improving COVID-19 detection using just Chest X-rays.
"Self-supervised contrastive learning of echocardiogram videos enables label-efficient cardiac disease diagnosis" by Gregory Holste, Evangelos Oikonomou, Bobak Mortazavi, Zhangyang Wang, and Rohan Khera
Rice Leaf Disease Classification using Convolution Bottleneck Attention Model
This repository contains the code to reproduce our exploratory analysis of a pan-cancer plasma proteomics dataset, including differential expression analysis and disease classification (https://doi.org/10.1038/s41467-023-39765-y). The data can be explored in the Human Protein Atlas: www.proteinatlas.org/humanproteome/disease.
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