scholarly journals Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears

2020 ◽  
Vol 24 (5) ◽  
pp. 1427-1438 ◽  
Author(s):  
Feng Yang ◽  
Mahdieh Poostchi ◽  
Hang Yu ◽  
Zhou Zhou ◽  
Kamolrat Silamut ◽  
...  
2020 ◽  
Vol 9 (1) ◽  
pp. 2057-2060

Detection of malaria disease is done by finding the presence of malaria parasite or plasmodium in blood smear. Here malaria parasites are detected in thick blood smears. This paper proposes a version to detect the presence of malaria parasite(plasmodium) in thick blood smears automatically with the help of deep learning and not using microscopy examinations and chemical tests. This detection will be done using two steps, that is, intensity-based screening which is the preprocessing step, the first step, that extracts candidates for processing, and next is customized convolutional neural network (CNN), the processing step, which takes the preprocessed images and detects whether malaria parasite is present or not. Hold-out(3:1) technique is used for evaluation of the model. The model has achieved an accuracy reaching 91%. The two preprocessing and processing steps improves object detection of the system. Malaria is usually detected using chemical tests and microscopy examinations. This process requires a lot of resources mainly laboratories. Parasitologists who are experienced are sometimes difficult to find, so manually counting the malaria parasites can be prone to major errors. Due to which the cost for testing and even time for malaria diagnosis increases drastically. Since the traditional process of malaria parasite in blood smears detection has many drawbacks it needs a sophisticated, accurate diagnosing equipment or system which has low cost. This system can be used in regions and areas where there are constraints on resources, time of people and cost which they can afford. This system provides many advantages to rural diagnostic centres where the supplies are limited and not easily accessible.


Author(s):  
K Venkata Shiva Rama Krishna Reddy ◽  
◽  
S Phani Kumar ◽  

Malaria parasitized detection is very important to detect as there are so many deaths due to false detection of malaria in medical reports. So analysis has gained a lot of attention in recent years. Detection of malaria is important as fast as possible because detecting malaria is difficult in blood smears. Our idea is to build a transfer learning model and detect the thick blood smears whether the presence of malaria parasites in a drop of blood. The data consists of 5000 each infected and uninfected data obtained from the NIH website. In this paper, I propose to use three different types of neural networks for the performance evaluation of the malaria data by transfer learning using CNN, VGG19, and fine-tuned VGG19. Transfer learning model performed well among various other models by achieving a precision of 98 percent and an f-1 score of 96 percent.


Author(s):  
Feng Yang ◽  
Nicolas Quizon ◽  
Hang Yu ◽  
Kamolrat Silamut ◽  
Richard Maude ◽  
...  

2019 ◽  
Vol 152 (Supplement_1) ◽  
pp. S116-S116
Author(s):  
Carlo Ledesma ◽  
Ma Gina Sadang

Abstract Human malaria, caused by four species of Plasmodium, namely P falciparum, P vivax, P malariae, and P ovale, remains a health problem of global concern, with one to two million deaths annually and risking about two billion people worldwide. Alternative ways of controlling the incidence of malaria through understanding the host’s immune response to monoinfection and the detection of the presence of asymptomatic malaria infection are the factors being addressed in this study. The determination of the possible existence of cross-antigenic stimulation is a matter of great significance for future research and development. The isolation of these antigenic structures may give the first step to the development of better vaccines that may protect the general population who are at risk of developing malaria. Prior to blood collection, a memorandum of agreement was signed between the researcher and the Iraya-Mangyan leaders of Abra de Ilog, Occidental Mindoro. A Certificate Precondition was issued by the National Commission of Indigenous Peoples, which was required by the Graduate School Ethics Review Committee. Determination of the presence of malaria parasite on blood samples of residents of two barangays in Abra de Ilog, Occidental Mindoro, was performed using two methods: microscopic examination of stained blood smears for the presence of malaria parasite and polymerase chain reaction. Blood smears were prepared and eventually stained using Giemsa and Dip Quick stains. The detection of 5 positive cases of malaria infection with ring/schizont stage among the 53 cases was a clear indication of positive asymptomatic cases. Nested PCR using Plasmodium spp.–specific primer as well as P falciparum–specific and P vivax–specific primers showed the absence of bands so that one of the recommendations in this study is the performance of real-time PRC using more sensitive primers. Levels of P falciparum and P vivax–specific immunoglobulin were measured using an enzyme-linked immunosorbent assay revealing a higher level of PF-specific IgG than PV-specific IgG. Whole blood samples were saved for future determinations such as real-time PCR, immunophenotypic analysis, and possible parasitic culture. Further similar studies may also be done by increasing the number of respondents as well as the areas of concern for a more extensive scope.


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