@inproceedings{93a15f2af7db4d178872e2a51f7fed28,
title = "Pigmented skin lesions classification using convolutional neural networks",
abstract = "In this paper we present an architecture for classification of pigmented skin lesions from dermatoscopic images. The architecture is using image pre-processing for natural hair removal and image segmentation for extraction of the skin lesion area. The segmented images were processed by a convolutional neural network classifier. The training process was done by using the Keras and TensorFlow python packets with CUDA supported. The best performance was achieved by a convolutional neural network architecture with three convolution layers and the classification accuracy was equal to 76.83%.",
keywords = "convolutional neural network, CUDA computing, dermatoscopy, image classification",
author = "Prasitthichai Naronglerdrit and Iosif Mporas and Isidoros Perikos and Michael Paraskevas",
year = "2019",
month = nov,
doi = "10.1109/BIA48344.2019.8967469",
language = "English",
isbn = "9781728147550",
series = "Proceedings of the International Conference on "Biomedical Innovations and Applications", BIA 2019",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
editor = "Valentina Markova and Todor Ganchev",
booktitle = "Proceedings of the International Conference on {"}Biomedical Innovations and Applications{"}",
address = "United States",
note = "2019 International Conference on Biomedical Innovations and Applications, BIA 2019 ; Conference date: 08-11-2019 Through 09-11-2019",
}