Comparison of CNNs and SVM for Detection of Activation in Malaria Cell Images

Year 2019, , 38 - 50, 31.12.2019

Jale Bektaş

https://doi.org/10.38061/idunas.632709

Cited By: 1

Abstract

Malaria is a disease
caused by parasites that are transmitted through the enzymes of Anophele
mosquito and cause symptoms in fatal danger. Thick and thin film microscopic
examination of smears taken from blood is the most reliable method for
diagnosis. In the manual examination of the smears, the expertise of examiner
and the quality of the smear significantly affect the accuracy of the
diagnosis. Malaria's automatic diagnosis of pattern recognition and
classification techniques on blood smear images is among the subjects of
research. In this study, well-known Convolutional Neural Networks include
InceptionV3, GoogLeNet, AlexNet, Resnet50, Vgg16 networks and six-fold cross
validation was applied and performance evaluations were performed with a
Machine Lerning method, Support Vector Machine. It was found that Deep Learning
methods achieved at least 10.08% of accuracy difference performance compared to
SVM based on the features of the input sample images. This difference has been
0.07 for F-Score, 0.06 for sensitivity.

Keywords

classification, CNN, SVM, , Pattern recognition

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