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%.
|Proceedings of the International Conference on &amp;amp;quot;Biomedical Innovations and Applications&amp;amp;quot;, BIA 2019
|2019 International Conference on Biomedical Innovations and Applications, BIA 2019
|8/11/19 → 9/11/19
- convolutional neural network
- CUDA computing
- image classification