Untangling Classification Methods for Melanoma Skin Cancer - Kumar, Ayushi; Vatsa, Avimanyou (Frontiers in Big Data, 2022)
Skin cancer is most common cancer in United State of America (USA). Skin cancer can affect anyone, regardless of skin color, race, gender, and age. The characteristics of skin lesion has an arbitrary shape, size, uneven and rough edge, and cannot be divided in half. Further, it is a leading cause of deaths worldwide. Every year, more than 5 million patients are newly diagnosed in USA. The deadliest and serious form of skin cancer is called melanoma. The diagnosis of melanoma has been done by visual examination and manual techniques by skilled doctors. It is time consuming process and highly prone to error. The skin images captured by dermoscopy eliminates the surface reflection of skin and gives better visualization of deeper levels of skin. In spite of these, image of skin lesion has many artifacts, noises, complex nature of lesion structure. Due to these complex natures of images, the border detection, feature extraction, and classification process is a complex problem. In order to identify and predict melanoma in early stage, there is need to classify images using better classification methods. Therefore, there is need to make an efficient, effective, and accurate melanoma identification, classification, and prediction such that it may be identified and classified in very early stage. The goal of this paper is to review and analyze the various deep neural network-based classification algorithms on skin images (ISIC dataset). Also, the performance algorithms are compared using five different parameters including ROC.