A comprehensive review on step-based skin cancer detection using machine learning and deep learning methods

Abstract

Skin cancer is one of the most frequent and deadly form of cancer. Essentially, it is an abnormal growth of skin cells that primarily occurs after contaminated hands with the sun. These days, it also appears on skin surfaces that are not exposed to sunlight. Skin cancer is smoothly curable only if it is diagnosed in its initial days. There are some prominent types of skin cancer named as melanoma, squamous cell carcinoma, basal cell carcinoma, and many others. Many machine learning and deep learning methods have been developed to interpret medical images, specifically those of skin lesions, it is difficult and tiresome to analyze these to find cancer manually. Computer-aided diagnosis systems have two essential procedures: classification and segmentation of lesions. These two procedures improve the quality of features retrieved from medical images. An overview of some methods used to diagnose skin cancer is provided to identify the most efficient preprocessing, segmentation, feature extraction, and classification of medical images. Various research methods for specific skin cancer classification are also explored in this study. A further hurdle to creating an optimal diagnosis algorithm is the absence of a dataset on skin cancer. In order to assist researchers in developing useful algorithms that rapidly and accurately diagnose skin cancer, the study offers to provide a current overview of the proposed solutions to the issues in skin cancer detection. We gathered the results in tabular form after analyzing the efficiency of the most recent research based on a variety of factors, including techniques, and the performance of the applied datasets. We have discussed the current Deep Learning and Machine Learning techniques for detecting skin cancer along with their limitations. Along with outlining the various assessment metrics, we have also discussed the research gaps and challenges, such as imbalanced datasets, intra-class variance, inter-class similarity, etc., in skin cancer detection. The survey demonstrates its superiority over various other surveys currently in use.