A skin cancer diagnosis system for dermoscopy images according to deep training and metaheuristics. (May 2023)

• Record Type: Journal Article
• Authors: Huang, Qirui; Ding, Huan; Rashid Sheykhahmad, Fatima
• Is Part Of: Biomedical signal processing and control. Volume 83(2023)
• Publication Date: 2023-05
• Subjects: Skin cancer -- Cancer diagnosis system -- Deep Belief Network -- Modified Electromagnetic Field Optimization Algorithm; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2023.104705
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A new optimal CAD system for the diagnosis of the skin cancer. The idea is to provide an optimal Deep Believe Network (DBN), based on metaheuristics. A new Modified version of Electromagnetic Field Optimization Algorithm is used for optimization. The method is validated by a comparison between its results and some other related works. Abstract: One of the most common kinds of cancer in the United States is skin cancer. This kind of cancer occurs when the cells that make up the skin layer grow uncontrollably and divide rapidly. To avoid the skin cancer harms, it is better to detect it in the early stages. Due to complexity of the diagnosis, some times, the experts provide an incorrect diagnosis about this cancer. Therefore, proposing a computer-aided system, not as an independent system, but as an auxiliary tool along with the physicians, can help them in more accurate detection of this kind of cancer. This study introduced an optimal and efficient computer-assisted system for skin cancer analysis. Here, deep training and metaheuristic techniques are used for this purpose. The main idea is to provide a Deep Belief Network (DBN), based on an improved metaheuristic technique, called Modified Electromagnetic Field Optimization Algorithm (MEFOA) to provide a strong diagnosis system for the images of skin cancer. The function of the proposed method is proved through a comparison between its results and some other related works.