Skin cancer is one of the most common types of cancer in humans, it covers about one third of all neoplasms. Within skin cancer we find basal cell carcinoma (BCC), this being the most frequent type of cancer worldwide. Solutions with convolutional neural networks generally use the Softmax layer (classic model) to perform a BCC classification, however, in other similar fields such as image classification of microscopic bacteria they have replaced this Softmax layer with a support vector machine (SVM) achieving a better result. Given this, we propose a hybrid model of convolutional neural network and a support vector machine (CNN+SVM) to classify the BCC. Our model is composed of 4 convolution blocks with 32, 64 and 128 filters to carry out the extraction of characteristics and then pass it to the classifier, to which the L1-SVM loss function is implemented. The average results obtained for the CNN+SVM hybrid model were measured with the precision, accuracy, recall and F1-score metrics, obtaining 96.200%, 96.200%, 96.205% and 96.200% respectively compared to the classical model for the metrics of precision, accuracy, recall and F1-score where 95.661%, 95.673%, 95.661%, 95.660% respectively were obtained. The results show that the hybrid model achieves better results than the classic model to classify the BCC.
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- Basal cell carcinoma
- Convolutional neural network
- Deep learning
- Support vector machine