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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 microscopic examinations of blood cells known as parasite-based microscopy diagnostic testing. The accuracy of this manual diagnosis method is clearly affected by the level of microscopist’s experience, which makes this diagnosis method susceptible to manual error and time consuming. Diagnoses of diseases made using deep-learning methods have had great repercussions in the medical world, especially in recent years; and this indicates that the diagnosis of malaria can also be achieved by deep-learning methods. On the basis of this fact, this paper presents a novel deep-learning-based malaria disease detection technique. A convolutional neural network (CNN) architecture, which has 20 weighted layers is designed and proposed to identify parasitized microscopic images from uninfected microscopic images. A total of 27,558 thin blood cell images were used to train and test the CNN model, and 95.28% overall accuracy was obtained. The experimental results on large clinical dataset show the effectiveness of the proposed deep-learning method for malaria disease detection.
Cite this article as: E. Irmak, “A Novel Implementation of Deep-Learning Approach on Malaria Parasite Detection from Thin Blood Cell Images”, Electrica, vol. 21, no. 2, pp. 216-224, May, 2021.