Manuscript received April 24, 2023; revised May 25, 2023; accepted June 30, 2023.
Abstract—Malaria is an infectious disease caused by the Plasmodium parasite. In 2019, there were 229 million cases of malaria with a death toll of 400.900. Malaria cases increased in 2020 to 241 million people with the death toll reaching 627,000. Malaria diagnosis which is carried out by observing the patient’s blood sample requires experts and if it is not done correctly, misdiagnosis can occur. Deep Learning can be used to help diagnose Malaria by classifying thin blood smear images. In this study, transfer learning techniques were used on the Convolutional Neural Network to speed up the model training process and get high accuracy. The architecture used for Transfer Learning is EfficientNetB0. The training model is embedded in a pythonbased web application which is then deployed on the Google App Engine platform. This is done so that it can be used by experts to help diagnose. The training model has a training accuracy of 0.9664, a training loss of 0.0937, a validation accuracy of 0.9734, and a validation loss of 0.0816. Prediction results on test data have an accuracy of 96.8% and an F1- score value of 0.968.
Keywords—malaria detection, Convolution Neural Network (CNN), transfer learning
Cite: Windra Swastika, Benedictus J. Pradana, Romy B. Widodo, Rehmadanta Sitepu, and Gregorius G. Putra, "Web-Based Application for Malaria Parasite Detection Using Thin-Blood Smear Images," Journal of Image and Graphics, Vol. 11, No. 3, pp. 288-293, September 2023.
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