Title:	COMPARATIVE EVALUATIONS OF CNN BASED NETWORKS FOR SKIN LESION CLASSIFICATION
Author(s):	Evgin Goceri and Ayse Akman Karakas
ISBN:	978-989-8704-21-4
Editors:	Yingcai Xiao, Ajith P. Abraham and Jörg Roth
Year:	2020
Edition:	Single
Keywords:	VGG, GoogleNet, ResNet, Inception, Skin Disease, Lesion Classification
Type:	Short
First Page:	237
Last Page:	242
Language:	English
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Paper Abstract:	The aim of this work is to classify Hemangioma, Rosacea and Acne Vulgaris diseases from digital colored photographs automatically. To determine the most appropriate deep neural network for this multi-class classification, network architectures have been examined. To perform a meaningful comparison of deep networks, they should be (i) implemented with the same parameters, (ii) applied with the same activation, loss and optimization functions, (iii) trained and tested with the same datasets, (iv) run on computers having the same hardware configurations. Therefore, in this work, five deep networks, which are applied widely in image classification, have been used to compare their performances by considering these factors. Those networks are VGG16, VGG19, GoogleNet, InceptionV3 and ResNet101. Comparative evaluations of the results obtained from these networks have been performed in terms of accuracy, precision and specificity. F1 score and Matthew’s correlation coefficient values have also been computed. Experimental results indicated that ResNet101 architecture can classify images used in this study with higher accuracy (77.72%) than the others.
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