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Skin lesions known as naevi exhibit diverse characteristics such as size,shape, and colouration. The concept of an "Ugly Duckling Naevus" comes intoplay when monitoring for melanoma, referring to a lesion with distinctivefeatures that sets it apart from other lesions in the vicinity. As lesionswithin the same individual typically share similarities and follow apredictable pattern, an ugly duckling naevus stands out as unusual and mayindicate the presence of a cancerous melanoma. Computer-aided diagnosis (CAD)has become a significant player in the research and development field, as itcombines machine learning techniques with a variety of patient analysismethods. Its aim is to increase accuracy and simplify decision-making, allwhile responding to the shortage of specialized professionals. These automatedsystems are especially important in skin cancer diagnosis where specialistavailability is limited. As a result, their use could lead to life-savingbenefits and cost reductions within healthcare. Given the drastic change insurvival when comparing early stage to late-stage melanoma, early detection isvital for effective treatment and patient outcomes. Machine learning (ML) anddeep learning (DL) techniques have gained popularity in skin cancerclassification, effectively addressing challenges, and providing resultsequivalent to that of specialists. This article extensively covers modernMachine Learning and Deep Learning algorithms for detecting melanoma andsuspicious naevi. It begins with general information on skin cancer anddifferent types of naevi, then introduces AI, ML, DL, and CAD. The article thendiscusses the successful applications of various ML techniques likeconvolutional neural networks (CNN) for melanoma detection compared todermatologists' performance. Lastly, it examines ML methods for UD naevusdetection and identifying suspicious naevi.

Application of Machine Learning in Melanoma Detection and the Identification of 'Ugly Duckling' and Suspicious Naevi: A Review