Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture

Yeniay, Özgür; Ozaltin, Oznur

Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture

Oznur Ozaltin, (Hacettepe Üniversitesi, İstatistik Bölümü, Ankara, Türkiye)

Özgür YENİAY (Hacettepe Üniversitesi, İstatistik Bölümü, Ankara, Türkiye)

Communications Faculty of Sciences University of Ankara Series A1: Mathematics and Statistics

3 1

Yıl: 2023 Cilt: 72 Sayı: 2 Sayfa Aralığı: 482 - 499 Metin Dili: İngilizce İndeks Tarihi: 29-06-2023

Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture

Öz:

Monkeypox has recently become an endemic disease that threatens the whole world. The most distinctive feature of this disease is occurring skin lesions. However, in other types of diseases such as chickenpox, measles, and smallpox skin lesions can also be seen. The main aim of this study was to quickly detect monkeypox disease from others through deep learning approaches based on skin images. In this study, MobileNetv2 was used to determine in images whether it was monkeypox or non-monkeypox. To find splitting methods and optimization methods, a comprehensive analysis was performed. The splitting methods included training and testing (70:30 and 80:20) and 10 fold cross validation. The optimization methods as adaptive moment estimation (adam), root mean square propagation (rmsprop), and stochastic gradient descent momentum (sgdm) were used. Then, MobileNetv2 was tasked as a deep feature extractor and features were obtained from the global pooling layer. The Chi-Square feature selection method was used to reduce feature dimensions. Finally, selected features were classified using the Support Vector Machine (SVM) with different kernel functions. In this study, 10 fold cross validation and adam were seen as the best splitting and optimization methods, respectively, with an accuracy of 98.59%. Then, significant features were selected via the Chi-Square method and while classifying 500 features with SVM, an accuracy of 99.69% was observed.

Anahtar Kelime: Chi-Square method deep learning feature selection monkeypox optimization

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık

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APA	Ozaltin O, Yeniay Ö (2023). Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture. , 482 - 499.
Chicago	Ozaltin Oznur,Yeniay Özgür Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture. (2023): 482 - 499.
MLA	Ozaltin Oznur,Yeniay Özgür Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture. , 2023, ss.482 - 499.
AMA	Ozaltin O,Yeniay Ö Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture. . 2023; 482 - 499.
Vancouver	Ozaltin O,Yeniay Ö Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture. . 2023; 482 - 499.
IEEE	Ozaltin O,Yeniay Ö "Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture." , ss.482 - 499, 2023.
ISNAD	Ozaltin, Oznur - Yeniay, Özgür. "Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture". (2023), 482-499.

APA	Ozaltin O, Yeniay Ö (2023). Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture. Communications Faculty of Sciences University of Ankara Series A1: Mathematics and Statistics, 72(2), 482 - 499.
Chicago	Ozaltin Oznur,Yeniay Özgür Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture. Communications Faculty of Sciences University of Ankara Series A1: Mathematics and Statistics 72, no.2 (2023): 482 - 499.
MLA	Ozaltin Oznur,Yeniay Özgür Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture. Communications Faculty of Sciences University of Ankara Series A1: Mathematics and Statistics, vol.72, no.2, 2023, ss.482 - 499.
AMA	Ozaltin O,Yeniay Ö Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture. Communications Faculty of Sciences University of Ankara Series A1: Mathematics and Statistics. 2023; 72(2): 482 - 499.
Vancouver	Ozaltin O,Yeniay Ö Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture. Communications Faculty of Sciences University of Ankara Series A1: Mathematics and Statistics. 2023; 72(2): 482 - 499.
IEEE	Ozaltin O,Yeniay Ö "Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture." Communications Faculty of Sciences University of Ankara Series A1: Mathematics and Statistics, 72, ss.482 - 499, 2023.
ISNAD	Ozaltin, Oznur - Yeniay, Özgür. "Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture". Communications Faculty of Sciences University of Ankara Series A1: Mathematics and Statistics 72/2 (2023), 482-499.