A Machine Learning and Deep Learning based webapp used to predict multiple diseases.
Source files for building the IDM EMOD disease transmission model.
Detecting Malaria using Deep Learning 🦟🦠
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.
Malaria Detection using Deep Learning
Upscaling SV detection to a multi-population level.
dEploid is designed for deconvoluting mixed genomes with unknown proportions. Traditional ‘phasing’ programs are limited to diploid organisms. Our method modifies Li and Stephen’s algorithm with Markov chain Monte Carlo (MCMC) approaches, and builds a generic framework that allows haloptype searches in a multiple infection setting.
CNN-based malaria detection from blood cell microscope images — 95.43% test accuracy on NIH dataset (27,558 images)
Global Disease Database – Android app to gather images for disease detection
WebUI for the Reveal epidemiological surveillance platform
Application d’éducation sur le paludisme et d’accès aux services de santé au Congo. / Application of education on malaria and access to health services in Congo.
A generalized deep learning-based framework for assistance to the human malaria diagnosis from microscopic images
R Utility Functions for Data Acquisition, Preparation and Analysis in Subnational Tailoring of Malaria Interventions (SNT)
Design and development of Peptide drugs against falciparum Malaria and a Deep learning Web App for Malaria Diagnosis
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