Skin cancer diagnosis using CNN features with Genetic Algorithm and Particle Swarm Optimization methods

被引:0
作者
Basaran, Erdal [1 ,3 ]
Celik, Yuksel [2 ]
机构
[1] Agri Ibrahim Cecen Univ, Dept Comp Technol, Agri, Turkiye
[2] Karabuk Univ, Dept Comp Engn, Karabuk, Turkiye
[3] Agri Ibrahim Cecen Univ, Distance Educ Applicat & Res Ctr, Dept Comp Technol, Agri, TR-04100, Turkiye
基金
英国科研创新办公室;
关键词
Biomedical image processing; EfficientNetB0; Genetic Algorithm; Particle Swarm Optimization; skin cancer detection; CLASSIFICATION; DISEASE;
D O I
10.1177/01423312241253926
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Skin cancer is one of the most common types of cancer in the world. If skin cancer is not treated early, it also affects the diseased area under the skin and this threatens the treatment of the disease. In recent years, many diseases have been rapidly detected with high accuracy with artificial intelligence methods, and the treatment process has accelerated. Convolutional neural networks, one of the artificial intelligence methods, provide very detailed information about images, and extremely successful results are obtained in classifying images. In this study, first the data set was trained with the EfficientNetB0 model, which is one of the convolutional neural networks models. Then, with the fully connected layer of this model, deep features of the images were obtained. These deep features were obtained by selecting Particle Swarm Optimization and Genetic Algorithm optimization, and different feature combinations were created. Each of these selected feature sets was classified by the support vector machines method, and the best performance results were tried to be obtained. As a result, the success of the proposed model has been proven by obtaining an accuracy rate of 89.17%.
引用
收藏
页码:2706 / 2713
页数:8
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