An Ensemble of Statistical Metadata and CNN Classification of Class Imbalanced Skin Lesion Data

Autor:	Sachin Nayak, Shweta Vincent, Sumathi K, Om Prakash Kumar, Sameena Pathan
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	classification convolutional neural networks ensemble learning machine learning metadata Electrical engineering. Electronics. Nuclear engineering TK1-9971 Telecommunication TK5101-6720
Zdroj:	International Journal of Electronics and Telecommunications, Vol vol. 68, Iss No 2, Pp 251-257 (2022)
Druh dokumentu:	article
ISSN:	2081-8491 2300-1933
DOI:	10.24425/ijet.2022.139875
Popis:	Skin Cancer is one of the most widely present forms of cancer. The correct classification of skin lesions as malignant or benign is a complex process that has to be undertaken by experienced specialists. Another major issue of the class imbalance of data causes a bias in the results of classification. This article presents a novel approach to the usage of metadata of skin lesions' images to classify them. The usage of techniques addresses the problem of class imbalance to nullify the imbalances. Further, the use of a convolutional neural network (CNN) is proposed to finetune the skin lesion data classification. Ultimately, it is proven that an ensemble of statistical metadata analysis and CNN usage would result in the highest accuracy of skin color classification instead of using the two techniques separately.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/6c2deb4a6ec6478bac095db0c67af859 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF