A Novel Implementation of Deep-Learning Approach on Malaria Parasite Detection from Thin Blood Cell Images

IRMAK, Emrah

doi:10.5152/electrica.2020.21004

A Novel Implementation of Deep-Learning Approach on Malaria Parasite Detection from Thin Blood Cell Images

Emrah Irmak (Elektrik ve Elektronik Mühendisliği Bölümü, Alanya Alaaddin Keykubat Üniversitesi, Antalya, Türkiye)

Electrica

9 1

Yıl: 2021 Cilt: 21 Sayı: 2 Sayfa Aralığı: 216 - 224 Metin Dili: İngilizce DOI: 10.5152/electrica.2020.21004 İndeks Tarihi: 14-10-2021

A Novel Implementation of Deep-Learning Approach on Malaria Parasite Detection from Thin Blood Cell Images

Öz:

Malaria is known as an acute febrile disease caused by the bite of female Anopheles mosquitoes, and it manifests itself with symptoms such as headache,fever, chills, vomiting, and fatigue. The diagnosis of malaria is still based on manual identification of Plasmodium parasitized cells in microscopicexaminations of blood cells known as parasite-based microscopy diagnostic testing. The accuracy of this manual diagnosis method is clearly affectedby the level of microscopist’s experience, which makes this diagnosis method susceptible to manual error and time consuming. Diagnoses of diseasesmade using deep-learning methods have had great repercussions in the medical world, especially in recent years; and this indicates that the diagnosisof malaria can also be achieved by deep-learning methods. On the basis of this fact, this paper presents a novel deep-learning-based malaria diseasedetection technique. A convolutional neural network (CNN) architecture, which has 20 weighted layers is designed and proposed to identify parasitizedmicroscopic images from uninfected microscopic images. A total of 27,558 thin blood cell images were used to train and test the CNN model, and95.28% overall accuracy was obtained. The experimental results on large clinical dataset show the effectiveness of the proposed deep-learning methodfor malaria disease detection.

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APA	IRMAK E (2021). A Novel Implementation of Deep-Learning Approach on Malaria Parasite Detection from Thin Blood Cell Images. , 216 - 224. 10.5152/electrica.2020.21004
Chicago	IRMAK Emrah A Novel Implementation of Deep-Learning Approach on Malaria Parasite Detection from Thin Blood Cell Images. (2021): 216 - 224. 10.5152/electrica.2020.21004
MLA	IRMAK Emrah A Novel Implementation of Deep-Learning Approach on Malaria Parasite Detection from Thin Blood Cell Images. , 2021, ss.216 - 224. 10.5152/electrica.2020.21004
AMA	IRMAK E A Novel Implementation of Deep-Learning Approach on Malaria Parasite Detection from Thin Blood Cell Images. . 2021; 216 - 224. 10.5152/electrica.2020.21004
Vancouver	IRMAK E A Novel Implementation of Deep-Learning Approach on Malaria Parasite Detection from Thin Blood Cell Images. . 2021; 216 - 224. 10.5152/electrica.2020.21004
IEEE	IRMAK E "A Novel Implementation of Deep-Learning Approach on Malaria Parasite Detection from Thin Blood Cell Images." , ss.216 - 224, 2021. 10.5152/electrica.2020.21004
ISNAD	IRMAK, Emrah. "A Novel Implementation of Deep-Learning Approach on Malaria Parasite Detection from Thin Blood Cell Images". (2021), 216-224. https://doi.org/10.5152/electrica.2020.21004

APA	IRMAK E (2021). A Novel Implementation of Deep-Learning Approach on Malaria Parasite Detection from Thin Blood Cell Images. Electrica, 21(2), 216 - 224. 10.5152/electrica.2020.21004
Chicago	IRMAK Emrah A Novel Implementation of Deep-Learning Approach on Malaria Parasite Detection from Thin Blood Cell Images. Electrica 21, no.2 (2021): 216 - 224. 10.5152/electrica.2020.21004
MLA	IRMAK Emrah A Novel Implementation of Deep-Learning Approach on Malaria Parasite Detection from Thin Blood Cell Images. Electrica, vol.21, no.2, 2021, ss.216 - 224. 10.5152/electrica.2020.21004
AMA	IRMAK E A Novel Implementation of Deep-Learning Approach on Malaria Parasite Detection from Thin Blood Cell Images. Electrica. 2021; 21(2): 216 - 224. 10.5152/electrica.2020.21004
Vancouver	IRMAK E A Novel Implementation of Deep-Learning Approach on Malaria Parasite Detection from Thin Blood Cell Images. Electrica. 2021; 21(2): 216 - 224. 10.5152/electrica.2020.21004
IEEE	IRMAK E "A Novel Implementation of Deep-Learning Approach on Malaria Parasite Detection from Thin Blood Cell Images." Electrica, 21, ss.216 - 224, 2021. 10.5152/electrica.2020.21004
ISNAD	IRMAK, Emrah. "A Novel Implementation of Deep-Learning Approach on Malaria Parasite Detection from Thin Blood Cell Images". Electrica 21/2 (2021), 216-224. https://doi.org/10.5152/electrica.2020.21004