This study aims to develop a new multi-criteria decision analysis methodology for skin detector evaluation and benchmarking based on artificial intelligence models. Two experiments were conducted. The first experiment comprised two stages: (1) Adaptation of the best previous case of skin detection approach utilizes multi-agent learning based on different color spaces. This stage aimed to create a decision matrix of various color spaces, and three groups of criteria (i.e., reliability, time complexity, and error rate within dataset) to test, evaluate and benchmark the adapted skin detection approaches. (2) Performance of multiple evaluation criteria for skin detection engines, this stage included two key stages. First, the correlation between criteria to investigate their relationship and determine their degree of correlation. Second, the performance analysis of criteria to identify the factors that affect the behavior of each criterion. The second experiment utilized a new multi-criter.....

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Objective: This research aims to survey the e®orts of researchers in response to the new and disruptive technology of skin cancer apps, map the research landscape from the literature onto coherent taxonomy, and determine the basic characteristics of this emerging ¯eld. In addition,this research looks at the motivation behind using Smartphone apps in the diagnosis of skin cancer and in health care and the open challenges that impede the utility of this technology. This study o®ers valuable recommendations to improve the acceptance and use of medical apps in the literature. Methods: We conducted a comprehensive survey using the keywords \skin cancer," \apps," and \Smartphone" or \m-Health" in di®erent variations to ¯nd all the relevant articles in three major databases: Web of Science, Science Direct, and IEEE Xplore. These databases broadly cover medical and technical literature. Results: We found 110 articles after a comprehensive survey of the literature. Out of the 110 articles,.....

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