ABSTRACT
This review paper focuses upon the current trends in the field of Skin Cancer Detection and Recognition. According to various researches based on skin cancer it is increasing day by day, and the detection of skin cancer in the earlier stage increases the survival rate of the person. Segmentation of skin lesion from normal skin and analysis of its parameters such as symmetry, colour, size, shape, etc. are used to detect skin cancer and to distinguish benign skin cancer from melanoma. Extraction and image detection of a lesion part plays a very important role to detect the skin cancer in its earlier stage. This paper represents various techniques to detect and classify the lesion part of the skin. The given image undergoes certain images processing techniques such as removal of hairs and noise free background and blurring, after that lesion part undergoes the most appropriate and accurate multiple thresholding approach, and feature extraction by using KNN, CNN & ANN to differentiate normal skin from the cancerous part. The increasing rate of skin cancer cases, and expensive medical treatment require that its symptoms be diagnosed early as it is more curable in initial stages. This paper presents a systematic review of deep learning techniques for the early detection of skin cancer. Research papers published in well-reputed journals, relevant to the topic of skin cancer detection and recognition, were analysed.
