Skin cancer diagnosis based on optimized convolutional neural network

被引:153
|
作者
Zhang, Ni [1 ]
Cai, Yi-Xin [1 ]
Wang, Yong-Yong [1 ]
Tian, Yi-Tao [1 ]
Wang, Xiao-Li [2 ]
Badami, Benjamin [3 ]
机构
[1] Huazhong Univ Sci & Technol, Tongji Hosp, Tongji Med Coll, Dept Throrac Surg, 1095 Jiefang Ave, Wuhan 430030, Hubei, Peoples R China
[2] Huazhong Univ Sci & Technol, Tongji Hosp, Tongji Med Coll, Canc Biol Res Ctr, 1095 Jiefang Ave, Wuhan 430030, Hubei, Peoples R China
[3] Univ Georgia, Athens, GA 30602 USA
关键词
Skin cancer diagnosis; Deep learning; Convolutional neural networks; Whale optimization algorithm; Levy flight; FORECAST ENGINE; MELANOMA DIAGNOSIS; FEATURE-SELECTION; ALGORITHM; SEGMENTATION; CLASSIFICATION;
D O I
10.1016/j.artmed.2019.101756
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Early detection of skin cancer is very important and can prevent some skin cancers, such as focal cell carcinoma and melanoma. Although there are several reasons that have bad impacts on the detection precision. Recently, the utilization of image processing and machine vision in medical applications is increasing. In this paper, a new image processing based method has been proposed for the early detection of skin cancer. The method utilizes an optimal Convolutional neural network (CNN) for this purpose. In this paper, improved whale optimization algorithm is utilized for optimizing the CNN. For evaluation of the proposed method, it is compared with some different methods on two different datasets. Simulation results show that the proposed method has superiority toward the other compared methods.
引用
收藏
页数:7
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