@ARTICLE{Nayak_Sachin_An_2022,
 author={Nayak, Sachin and Vincent, Shweta and K, Sumathi and Kumar, Om Prakash and Pathan, Sameena},
 volume={vol. 68},
 number={No 2},
 journal={International Journal of Electronics and Telecommunications},
 pages={251-257},
 howpublished={online},
 year={2022},
 publisher={Polish Academy of Sciences Committee of Electronics and Telecommunications},
 abstract={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.},
 type={Article},
 title={An Ensemble of Statistical Metadata and CNN Classification of Class Imbalanced Skin Lesion Data},
 URL={http://www.czasopisma.pan.pl/Content/123374/PDF-MASTER/35-3346-Nayak-sk-b_new.pdf},
 doi={10.24425/ijet.2022.139875},
 keywords={classification, Convolutional Neural Networks, Ensemble Learning, machine learning, metadata},
}