# An Overview of Melanoma Detection in Dermoscopy Images Using Image Processing and Machine Learning

arXiv: https://arxiv.org/abs/1601.07843 · HTML: https://mlsanity.com/paper/1601.07843/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2016.

## Built on by (most cited first, 13 of 13)

- [Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification](https://mlsanity.com/paper/2109.09818/) (2021)
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- [Auditing Sybil: Explaining Deep Lung Cancer Risk Prediction Through Generative Interventional Attributions](https://mlsanity.com/paper/2602.02560/) (2026)
- [The Fast and Accurate Approach to Detection and Segmentation of Melanoma Skin Cancer using Fine-tuned Yolov3 and SegNet Based on Deep Transfer Learning](https://mlsanity.com/paper/2210.05167/) (2022)
- [Siamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasets](https://mlsanity.com/paper/2212.06130/) (2022)
- [When Medical Imaging Met Self-Attention: A Love Story That Didn't Quite Work Out](https://mlsanity.com/paper/2404.12295/) (2024)
- [Invariant Learning with Annotation-free Environments](https://mlsanity.com/paper/2504.15686/) (2025)
- [Melatect: A Machine Learning Model Approach For Identifying Malignant Melanoma in Skin Growths](https://mlsanity.com/paper/2109.03310/) (2021)
- [A Rotation Meanout Network with Invariance for Dermoscopy Image Classification and Retrieval](https://mlsanity.com/paper/2208.00627/) (2022)
- [Classification of Melanocytic Nevus Images using BigTransfer (BiT)](https://mlsanity.com/paper/2211.11872/) (2022)
- [Reducing Bias in Pre-trained Models by Tuning while Penalizing Change](https://mlsanity.com/paper/2404.12292/) (2024)

## Abstract (arXiv)

The incidence of malignant melanoma continues to increase worldwide. This cancer can strike at any age; it is one of the leading causes of loss of life in young persons. Since this cancer is visible on the skin, it is potentially detectable at a very early stage when it is curable. New developments have converged to make fully automatic early melanoma detection a real possibility. First, the advent of dermoscopy has enabled a dramatic boost in clinical diagnostic ability to the point that melanoma can be detected in the clinic at the very earliest stages. The global adoption of this technology has allowed accumulation of large collections of dermoscopy images of melanomas and benign lesions validated by histopathology. The development of advanced technologies in the areas of image processing and machine learning have given us the ability to allow distinction of malignant melanoma from the many benign mimics that require no biopsy. These new technologies should allow not only earlier detection of melanoma, but also reduction of the large number of needless and costly biopsy procedures. Although some of the new systems reported for these technologies have shown promise in preliminary trials, widespread implementation must await further technical progress in accuracy and reproducibility. In this paper, we provide an overview of computerized detection of melanoma in dermoscopy images. First, we discuss the various aspects of lesion segmentation. Then, we provide a brief overview of clinical feature segmentation. Finally, we discuss the classification stage where machine learning algorithms are applied to the attributes generated from the segmented features to predict the existence of melanoma.