文献类型：期刊文献

英文题名：Computer-aided diagnosis of skin cancer based on soft computing techniques

作者：Xu, Zhiying[1];Sheykhahmad, Fatima Rashid[2];Ghadimi, Noradin[2];Razmjooy, Navid[2]

机构：[1]Shaoxing Univ, Yuanpei Coll, Shaoxing 312000, Zhejiang, Peoples R China;[2]Islamic Azad Univ, Ardabil Branch, Young Researchers & Elite Club, Ardebil, Iran

年份：2020

卷号：15

期号：1

起止页码：860

外文期刊名：OPEN MEDICINE

收录：SCI-EXPANDED(收录号：WOS:000571083600001)、、Scopus(收录号：2-s2.0-85095873614)、WOS

语种：英文

外文关键词：skin cancer; image segmentation; feature extraction; feature selection; convolutional neural networks; classification; satin bowerbird optimization

外文摘要：Skin cancer is a type of disease in which malignant cells are formed in skin tissues. However, skin cancer is a dangerous disease, and an early detection of this disease helps the therapists to cure this disease. In the present research, an automatic computer-aided method is presented for the early diagnosis of skin cancer. After image noise reduction based on median filter in the first stage, a new image segmentation based on the convolutional neural network optimized by satin bowerbird optimization (SBO) has been adopted and its efficiency has been indicated by the confusion matrix. Then, feature extraction is performed to extract the useful information from the segmented image. An optimized feature selection based on the SBO algorithm is also applied to prune excessive information. Finally, a support vector machine classifier is used to categorize the processed image into the following two groups: cancerous and healthy cases. Simulations have been performed of the American Cancer Society database, and the results have been compared with ten different methods from the literature to investigate the performance of the system in terms of accuracy, sensitivity, negative predictive value, specificity, and positive predictive value.