Kaifeng Zhang¹, Zhao Qiu^1,*, Gengquan Xie¹, Jiale Lin¹, Tingting Zhang¹, Yingsheng Lian¹, Tao Chen¹, Yunlong He², Yu Yang²

Journal of Cyber Security, Vol.4, No.2, pp. 95-107, 2022, DOI:10.32604/jcs.2022.029451

Abstract Due to its unique security, blockchain technology is widely used in the financial field. Under the background of the rapid development of information technology and the rapid improvement of medical level, it is also a general trend to integrate blockchain technology into the medical field. According to the characteristics of blockchain and the research contents of many scholars on the application of blockchain in the medical field, this paper analyzes and summarizes the problems existing in the current development of blockchain, puts forward corresponding solutions, and looks forward to the further application of blockchain technology in the medical field. More >

Mustafa Musa Jaber^1,2,*, Salman Yussof¹, Amer S. Elameer³, Leong Yeng Weng¹, Sura Khalil Abd^2,6, Anand Nayyar^4,5

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2175-2190, 2022, DOI:10.32604/cmc.2022.023387

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 convolution network uses an automatic… More >