There are 0 repository under malaria topic.
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 🦟🦠
An R interface to open-access malaria data, hosted by the Malaria Atlas Project.
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.
A mobile app that will aid the Volunteer in sustaining life-saving malaria prevention tactics over their 2+ years of service.
Malaria Detection using Deep Learning
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.
Upscaling SV detection to a multi-population level.
Global Disease Database – Android app to gather images for disease detection
WebUI for the Reveal epidemiological surveillance platform
MOI and Allele Frequency Recovery from Noisy Polyallelic Genetics Data
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.
Design and development of Peptide drugs against falciparum Malaria and a Deep learning Web App for Malaria Diagnosis
Spatial individual-based model of malaria with a focus on drug resistance evolution.
A generalized deep learning-based framework for assistance to the human malaria diagnosis from microscopic images
Exploring image colour space transformations and augmentation for creating a classifier to characterise parasitized and uninfected RBCs. Proposes a CNN model that uses the Saturation of the HSV colour model to create a high quality classifier resulting in accuracies of 99.3% and above.
A mobile app that will aid the Volunteer in sustaining life-saving malaria prevention tactics over their 2+ years of service.
Multiple Disease Diagnosis System using Medical Images
Binary Classification of images of cells which are either uninfected or parasitized by malaria.
Malaria is a serious global health problem that affects millions of people each year. One of the challenges in diagnosing malaria is identifying infected cells from microscopic images of blood smears. Convolutional Neural Networks (CNNs) are a type of deep learning algorithm that have been used for image classification tasks etc
Tutorial materials as referenced in IDM's documentation for EMOD, an agent-based disease dynamics model.
Open source static cytometry, initially targeting P. falciparum malaria
The source code of malaria.one website.
Using a GPT3-based classifier to identify malaria papers reporting phenotypes
Implementation of a deep learning model for leukemia classification and malaria detection using CNNs. The model utilizes transfer learning with VGG19 and InceptionV3 architectures, trained on custom datasets. The code includes data preprocessing, model training, evaluation, and visualization of performance metrics. Achieved high accuracy
Companion repository for: "Comparative transcriptomics reveal differential gene expression among Plasmodium vivax geographical isolates and implications on erythrocyte invasion mechanisms" by Kepple, et al.
A pipeline for variant calling from P. falciparum short reads generated from Illumina and ONT libraries
This repository contains the source and data for the HRP2/3 Deletion Risk Explorer, deletion risk analysis files, and additional data in support of forthcoming preprint.
Neural network that recognizes malaria in blood cells by image
Team Flask Capstone Project - Hamoye Winter '23 Cohort