A Comparative Approach for Skin Cancer Detection Using Artificial Bee Colony

The importance of the Artificial Bee Colony (ABC) algorithm is increasing day by day in various domains. Medical image analysis holds the attention of researchers in this field because these images are important in the field of human health. The occurrence of skin cancer is increasing rapidly, leading to a higher mortality rate. If the cancer is detected in the early stage, then the chances of survival are increased. In this work, ABC is used on available datasets from various sources. This dataset contains images that are impacted by some anomalies. Databases are formed by including images sourced from various sites and vary in type of lighting, and resolution. Morphological filtering was used to remove image noise and the ABC algorithm is used to find the optimal threshold for skin cancer detection. To demonstrate the strength of this work, the comparisons are made with different measures with existing techniques for skin cancer recognition.