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blood smear

Malaria parasite detection in thick blood smear microscopic images using modified YOLOV3 and YOLOV4 models

March 13, 2021 - 17:05 -- Open Access
Author(s): 
Abdurahman F, Fante KA, Aliy M
Reference: 
BMC Bioinformatics. 2021 Mar 8;22(1):112

Manual microscopic examination of Leishman/Giemsa stained thin and thick blood smear is still the “gold standard” for malaria diagnosis. One of the drawbacks of this method is that its accuracy, consistency, and diagnosis speed depend on microscopists’ diagnostic and technical skills. It is difficult to get highly skilled microscopists in remote areas of developing countries. To alleviate this problem, in this paper, we propose to investigate state-of-the-art one-stage and two-stage object detection algorithms for automated malaria parasite screening from microscopic image of thick blood slides.

NOT Open Access | Machine learning model for predicting malaria using clinical information

February 3, 2021 - 15:04 -- NOT Open Access
Author(s): 
Lee YW, Choi JW, Shin EH
Reference: 
Comput Biol Med. 2021 Feb;129:104151

Rapid diagnosing is crucial for controlling malaria. Various studies have aimed at developing machine learning models to diagnose malaria using blood smear images; however, this approach has many limitations. This study developed a machine learning model for malaria diagnosis using patient information.

Deep Learning Based Automatic Malaria Parasite Detection from Blood Smear and its Smartphone Based Application

May 25, 2020 - 07:58 -- Open Access
Author(s): 
Fuhad KMF, Tuba JF, Sarker MRA, Momen S, Mohammed N, Rahman T
Reference: 
Diagnostics (Basel). 2020 May 20; 10(5):E329

Malaria is a life-threatening disease that is spread by the Plasmodium parasites. It is detected by trained microscopists who analyze microscopic blood smear images. Modern deep learning techniques may be used to do this analysis automatically. The need for the trained personnel can be greatly reduced with the development of an automatic accurate and efficient model.

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