Analyzing Malaria Disease Using Effective Deep Learning Approach.

Krit Sriporn,Cheng-Fa Tsai,Paohsi Wang,Chia-En Tsai

doi:10.3390/diagnostics10100744

Abstract

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.

Highlights

IntroductionThe World Health Organization (WHO), through an estimation of the demography in its World
The World Health Organization (WHO), through an estimation of the demography in its WorldMalaria Report 2018, reported that there were 212 million patients and as many as 435,000 patient deaths worldwide from malaria
Masud et al aimed at developing a convolutional neural network (CNN) model by fine-tuning the hyperparameter of the pretrained model and improving performance by using cyclical learning rates-triangular2, which finds the best learning rate of Stochastic gradient descent (SGD) to improve the performance for malaria detection [29]

Summary

Introduction

The World Health Organization (WHO), through an estimation of the demography in its World. Malaria Report 2018, reported that there were 212 million patients and as many as 435,000 patient deaths worldwide from malaria. In tropical Africa, it is estimated that 3.1 billion US dollars are lost per year due to increased public health expenditures, adversely affecting tourism [1,2]. Malaria is a disease caused by the Plasmodium parasite that spreads throughout the human body through the bites of female anopheles, which can spread to others from mosquitoes that bite malaria patients. In addition to being transmitted from mother to fetus, patients may be infected with malaria through blood transfusions or through sharing syringes [3,4]. The symptoms of an infected person are similar to the flu and can include other

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Conclusion