Simulating Malaria Detection in Laboratories using Deep Learning
Malaria is usually diagnosed by a microbiologist by examining a small sample of blood smear. Reducing mortality from malaria infection is possible if it is diagnosed early and followed with appropriate treatment. While the WHO has set audacious goals of reducing malaria incidence and mortality rates by 90% in 2030 and eliminating malaria in 35 countries by that time, it still remains a difficult challenge. Computer-assisted diagnostics are on the rise these days as they can be used effectively as a primary test in the absence of or providing assistance to a physician or pathologist. The purpose of this paper is to describe an approach to detecting, localizing and counting parasitic cells in blood sample images towards easing the burden on healthcare workers.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Novel Exploration Techniques (NETs) for Malaria Policy Interventions
The task of decision-making under uncertainty is daunting, especially for problems which have significant complexity. Healthcare policy makers across the globe are facing problems under challenging constraints, with limi…
Decision MakingDecision Making Under UncertaintyA deep architecture based on attention mechanisms for effective end-to-end detection of early and mature malaria parasites in a realistic scenario
Background: Malaria is a critical and potentially fatal disease caused by the Plasmodium parasite and is responsible for more than 600,000 deaths globally. Early and accurate detection of malaria parasites is crucial f…
DiagnosticMalaria Falciparum DetectionMalaria Malariae DetectionMalaria Ovale Detection+5Malaria detection in Segmented Blood Cell using Convolutional Neural Networks and Canny Edge Detection
We apply convolutional neural networks to identify between malaria infected and non-infected segmented cells from the thin blood smear slide images. We optimize our model to find over 95% accuracy in malaria cell detecti…
Cell DetectionEdge DetectionMalaria detection using Deep Convolution Neural Network
The latest WHO report showed that the number of malaria cases climbed to 219 million last year, two million higher than last year. The global efforts to fight malaria have hit a plateau and the most significant underlyin…
DiagnosticMalaria Detection and Classificaiton
Malaria is a disease of global concern according to the World Health Organization. Billions of people in the world are at risk of Malaria today. Microscopy is considered the gold standard for Malaria diagnosis. Microscop…