The skin cancer classification using deep convolutional neural network - Dorj, Ulzii-Orshikh; Lee, Keun-Kwang; Choi, Jae-Young; Lee, Malrey (Multimedia tools and applications, 2018)
This paper addresses the demand for an intelligent and rapid classification system of skin cancer using contemporary highly-efficient deep convolutional neural network. In this paper, we mainly focus on the task of classifying the skin cancer using ECOC SVM, and deep convolutional neural network. RGB images of the skin cancers are collected from the Internet. Some collected images have noises such as other organs, and tools. These images are cropped to reduce the noise for better results. In this paper, an existing, and pre-trained AlexNet convolutional neural network model is used in extracting features. A ECOC SVM clasifier is utilized in classification the skin cancer. The results are obtained by executing a proposed algorithm with a total of 3753 images, which include four kinds of skin cancers images. The implementation result shows that maximum values of the average accuracy, sensitivity, and specificity are 95.1 (squamous cell carcinoma), 98.9 (actinic keratosis), 94.17 (squamous cell carcinoma), respectively. Minimum values of the average in these measures are 91.8 (basal cell carcinoma), 96.9 (Squamous cell carcinoma), and 90.74 (melanoma), respectively.