# Automatic Skin Lesion Segmentation using Semi-supervised Learning Technique

arXiv: https://arxiv.org/abs/1703.04301 · HTML: https://mlsanity.com/paper/1703.04301/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2017.

## Built on by (most cited first, 1 of 1)

- [A Region of Interest Focused Triple UNet Architecture for Skin Lesion Segmentation](https://mlsanity.com/paper/2311.12581/) (2023)

## Abstract (arXiv)

Skin cancer is the most common of all cancers and each year million cases of skin cancer are treated. Treating and curing skin cancer is easy, if it is diagnosed and treated at an early stage. In this work we propose an automatic technique for skin lesion segmentation in dermoscopic images which helps in classifying the skin cancer types. The proposed method comprises of two major phases (1) preprocessing and (2) segmentation using semi-supervised learning algorithm. In the preprocessing phase noise are removed using filtering technique and in the segmentation phase skin lesions are segmented based on clustering technique. K-means clustering algorithm is used to cluster the preprocessed images and skin lesions are filtered from these clusters based on the color feature. Color of the skin lesions are learned from the training images using histograms calculations in RGB color space. The training images were downloaded from the ISIC 2017 challenge website and the experimental results were evaluated using validation and test sets.