A Deep Transfer Learning-Based Comparative Study for Detection of Malaria Disease

Emel Soylu

doi:10.35377/saucis...1197119

EN

A Deep Transfer Learning-Based Comparative Study for Detection of Malaria Disease

Abstract

Malaria is a disease caused by a parasite. The parasite is transmitted to humans through the bite of infected mosquitoes. Thousands of people die every year due to malaria. When this disease is diagnosed early, it can be fully treated with medication. Diagnosis of malaria can be made according to the presence of parasites in the blood taken from the patient. In this study, malaria detection and diagnosis study were performed using The Malaria dataset containing a total of 27,558 cell images with samples of equally parasitized and uninfected cells from thin blood smear slide images of segmented cells. It is possible to detect malaria from microscopic blood smear images via modern deep learning techniques. In this study, 5 of the popular convolutional neural network architectures for malaria detection from cell images were retrained to find the best combination of architecture and learning algorithm. AlexNet, GoogLeNet, ResNet-50, MobileNet-v2, VGG-16 architectures from pre-trained networks were used, their hyperparameters were adjusted and their performances were compared. In this study, a maximum 96.53% accuracy rate was achieved with MobileNet-v2 architecture using the adam learning algorithm

Keywords

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Details

Primary Language

English

Subjects

Software Engineering (Other)

Journal Section

Research Article

Authors

Emel Soylu ^*
0000-0003-2774-9778
Türkiye

Publication Date

December 31, 2022

Submission Date

October 31, 2022

Acceptance Date

November 30, 2022

Published in Issue

Year 1970 Volume: 5 Number: 3

DOI

https://doi.org/10.35377/saucis...1197119

IZ

https://izlik.org/JA77JH93AZ

Cite

RIS / Bibtex

APA

Soylu, E. (2022). A Deep Transfer Learning-Based Comparative Study for Detection of Malaria Disease. Sakarya University Journal of Computer and Information Sciences, 5(3), 427-447. https://doi.org/10.35377/saucis...1197119

AMA

1.Soylu E. A Deep Transfer Learning-Based Comparative Study for Detection of Malaria Disease. SAUCIS. 2022;5(3):427-447. doi:10.35377/saucis.1197119

Chicago

Soylu, Emel. 2022. “A Deep Transfer Learning-Based Comparative Study for Detection of Malaria Disease”. Sakarya University Journal of Computer and Information Sciences 5 (3): 427-47. https://doi.org/10.35377/saucis. 1197119.

EndNote

Soylu E (December 1, 2022) A Deep Transfer Learning-Based Comparative Study for Detection of Malaria Disease. Sakarya University Journal of Computer and Information Sciences 5 3 427–447.

IEEE

[1]E. Soylu, “A Deep Transfer Learning-Based Comparative Study for Detection of Malaria Disease”, SAUCIS, vol. 5, no. 3, pp. 427–447, Dec. 2022, doi: 10.35377/saucis...1197119.

ISNAD

Soylu, Emel. “A Deep Transfer Learning-Based Comparative Study for Detection of Malaria Disease”. Sakarya University Journal of Computer and Information Sciences 5/3 (December 1, 2022): 427-447. https://doi.org/10.35377/saucis. 1197119.

JAMA

1.Soylu E. A Deep Transfer Learning-Based Comparative Study for Detection of Malaria Disease. SAUCIS. 2022;5:427–447.

MLA

Soylu, Emel. “A Deep Transfer Learning-Based Comparative Study for Detection of Malaria Disease”. Sakarya University Journal of Computer and Information Sciences, vol. 5, no. 3, Dec. 2022, pp. 427-4, doi:10.35377/saucis. 1197119.

Vancouver

1.Emel Soylu. A Deep Transfer Learning-Based Comparative Study for Detection of Malaria Disease. SAUCIS. 2022 Dec. 1;5(3):427-4. doi:10.35377/saucis. 1197119