Mustafa Musa Jaber1,2,*, Salman Yussof1, Amer S. Elameer3, Leong Yeng Weng1, Sura Khalil Abd2,6, Anand Nayyar4,5
CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2175-2190, 2022, DOI:10.32604/cmc.2022.023387
- 29 March 2022
Abstract Artificial intelligence plays an essential role in the medical and health industries. Deep convolution networks offer valuable services and help create automated systems to perform medical image analysis. However, convolution networks examine medical images effectively; such systems require high computational complexity when recognizing the same disease-affected region. Therefore, an optimized deep convolution network is utilized for analyzing disease-affected regions in this work. Different disease-related medical images are selected and examined pixel by pixel; this analysis uses the gray wolf optimized deep learning network. This method identifies affected pixels by the gray wolf hunting process. The More >
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