Evaluation of Big Data based CNN Models in Classification of Skin Lesions with Melanoma
Abstract
This chapter presents a methodology for diagnosis of pigmented skin lesions using convolutional neural networks. The architecture is based on convolu-tional neural networks and it is evaluated using new CNN models as well as re-trained modification of pre-existing CNN models were used. The experi-mental results showed that CNN models pre-trained on big datasets for gen-eral purpose image classification when re-trained in order to identify skin le-sion types offer more accurate results when compared to convolutional neural network models trained explicitly from the dermatoscopic images. The best performance was achieved by re-training a modified version of ResNet-50 convolutional neural network with accuracy equal to 93.89%. Analysis on skin lesion pathology type was also performed with classification accuracy for melanoma and basal cell carcinoma being equal to 79.13% and 82.88%, respectively.
Keywords
Cite
@article{arxiv.2007.05446,
title = {Evaluation of Big Data based CNN Models in Classification of Skin Lesions with Melanoma},
author = {Prasitthichai Naronglerdrit and Iosif Mporas},
journal= {arXiv preprint arXiv:2007.05446},
year = {2020}
}
Comments
Series Title: Studies in Computational Intelligence, Book Title: Deep Learning for Cancer Diagnosis, Series Volume: 908, DOI: 10.1007/978-981-15-6321-8, eBook ISBN: 978-981-15-6321-8