Systematic Review: Early Melanoma Detection Using Machine learning approach (Preprint)

Abstract

The deadliest skin cancer is melanoma. Early detection of melanoma increases the chances of survival. Because early detection of melanoma is important to reduce mortality, many computer-assisted diagnostic methods for detecting melanoma have been proposed in the literature. This article details the current state of research on melanoma detection using computer-aided diagnosis.

The aim of this review is to summarize and compare advanced dermoscopic algorithms used for classification of skin lesion and to notify important issues affecting the classification procedure.

This systematic review was conducted using the latest statistical data and by reading and analyzing several scientific papers on the subject. The datasets used, in conjunction with the use of the best performance evaluation approach, help us to obtain good results. This is done after comparisons and comparisons based on the entire detection process.

The main finding of this paper is to testify how the three building blocks (dataset, detection techniques, and evaluation methods) used to arrive at a better result for detecting melanoma successfully.

The outcomes of this review is to indicate a procedures of detecting techniques and parameter selection for evaluating the degree of classifying and detecting a given skin lesion successfully, especially at its earliest stage.

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