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Open AccessArticle

Analyzing Malaria Disease Using Effective Deep Learning Approach

1
Department of Tropical Agriculture and International Cooperation, National Pingtung University of Science and Technology, Neipu, Pingtung 91201, Taiwan
2
Department of Information Technology, Suratthani Rajabhat University, Suratthani 84100, Thailand
3
Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
4
Department of Biochemistry and Molecular Biology, National Cheng Kung University, Tainan 70101, Taiwan
5
Department of Food and Beverage Management, Cheng Shiu University, Kaohsiung 83347, Taiwan
*
Author to whom correspondence should be addressed.
Diagnostics 2020, 10(10), 744; https://doi.org/10.3390/diagnostics10100744
Received: 13 August 2020 / Revised: 23 September 2020 / Accepted: 23 September 2020 / Published: 24 September 2020
(This article belongs to the Special Issue Deep Learning for Computer-Aided Diagnosis in Biomedical Imaging)
Medical tools used to bolster decision-making by medical specialists who offer malaria treatment include image processing equipment and a computer-aided diagnostic system. Malaria images can be employed to identify and detect malaria using these methods, in order to monitor the symptoms of malaria patients, although there may be atypical cases that need more time for an assessment. This research used 7000 images of Xception, Inception-V3, ResNet-50, NasNetMobile, VGG-16 and AlexNet models for verification and analysis. These are prevalent models that classify the image precision and use a rotational method to improve the performance of validation and the training dataset with convolutional neural network models. Xception, using the state of the art activation function (Mish) and optimizer (Nadam), improved the effectiveness, as found by the outcomes of the convolutional neural model evaluation of these models for classifying the malaria disease from thin blood smear images. In terms of the performance, recall, accuracy, precision, and F1 measure, a combined score of 99.28% was achieved. Consequently, 10% of all non-dataset training and testing images were evaluated utilizing this pattern. Notable aspects for the improvement of a computer-aided diagnostic to produce an optimum malaria detection approach have been found, supported by a 98.86% accuracy level. View Full-Text
Keywords: activation function (Mish); image processing; image classification; malaria; convolutional neural network; optimization methods; deep learning activation function (Mish); image processing; image classification; malaria; convolutional neural network; optimization methods; deep learning
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MDPI and ACS Style

Sriporn, K.; Tsai, C.-F.; Tsai, C.-E.; Wang, P. Analyzing Malaria Disease Using Effective Deep Learning Approach. Diagnostics 2020, 10, 744. https://doi.org/10.3390/diagnostics10100744

AMA Style

Sriporn K, Tsai C-F, Tsai C-E, Wang P. Analyzing Malaria Disease Using Effective Deep Learning Approach. Diagnostics. 2020; 10(10):744. https://doi.org/10.3390/diagnostics10100744

Chicago/Turabian Style

Sriporn, Krit; Tsai, Cheng-Fa; Tsai, Chia-En; Wang, Paohsi. 2020. "Analyzing Malaria Disease Using Effective Deep Learning Approach" Diagnostics 10, no. 10: 744. https://doi.org/10.3390/diagnostics10100744

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