142 results for “topic:malaria”
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.
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)
Alistair Miles' blog
Global Disease Database – Android app to gather images for disease detection
No description provided.
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.
No description provided.
MOI and Allele Frequency Recovery from Noisy Polyallelic Genetics Data
A generalized deep learning-based framework for assistance to the human malaria diagnosis from microscopic images
Malaria outbreak prediction more specifically in zanzibar tanzania
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.
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.
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
MicrOscopic VisuAlization of BLood cElls for the Detection of Malaria and CD4+
Multiple Disease Diagnosis System using Medical Images
Binary Classification of images of cells which are either uninfected or parasitized by malaria.
Tutorial materials as referenced in IDM's documentation for EMOD, an agent-based disease dynamics model.
Mobile sequencing and analysis in real-time
Analysis code for malaria
Creates a construct developed by the Niles Lab at MIT, designed to deliver a 3' UTR post-transcriptional regulatory element payload to a specific given gene in Plasmodium falciparum.
Open source static cytometry, initially targeting P. falciparum malaria