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The percentage of people affected by skin cancer has been rising in recent years. Melanoma is identified as the most dangerous and life-threatening among the three types of skin cancer since it causes more deaths than the other two over time. According to the expertise, if the melanoma case is discovered at an early stage, the death rate may be decreased. Due to the skin lesion’s complexity, it is challenging to identify melanoma at an early stage. In this work, an automated assistant system is suggested to help doctors in identifying melanoma effectively at an early stage. Because pixel intensity values include distinctive and useful features in an image, hence the pixel intensity value estimation (IVE) model is embedded with a transfer learning network for efficient Melanoma detection. Four popular transfer learning models have been analyzed to derive the best-performed model in Melanoma detection (MD). Finally …