Malaria 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. Microscopic assessment of blood samples requires the need of trained professionals who at times are not available in rural areas where Malaria is a problem. Full automation of Malaria diagnosis is a challenging task. In this work, we put forward a framework for diagnosis of malaria. We adopt a two layer approach, where we detect infected cells using a Faster-RCNN in the first layer, crop them out, and feed the cropped cells to a seperate neural network for classification. The proposed methodology was tested on an openly available dataset, this will serve as a baseline for the future methods as currently there is no common dataset on which results are reported for Malaria Diagnosis.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A 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…
DiagnosticPlasmoID: A dataset for Indonesian malaria parasite detection and segmentation in thin blood smear
Indonesia holds the second-highest-ranking country for the highest number of malaria cases in Southeast Asia. A different malaria parasite semantic segmentation technique based on a deep learning approach is an alternati…
SegmentationSemantic SegmentationAutomated Web-Based Malaria Detection System with Machine Learning and Deep Learning Techniques
Malaria parasites pose a significant global health burden, causing widespread suffering and mortality. Detecting malaria infection accurately is crucial for effective treatment and control. However, existing automated de…
Transfer Learning