# Skin Lesion Segmentation and Classification for ISIC 2018 Using Traditional Classifiers with Hand-Crafted Features

arXiv: https://arxiv.org/abs/1807.07001 · HTML: https://mlsanity.com/paper/1807.07001/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2018.

## Built on by (most cited first, 3 of 3)

- [Uncertainty Quantification for Machine Learning in Healthcare: A Survey](https://mlsanity.com/paper/2505.02874/) (2025)
- [Skin lesion segmentation and classification using deep learning and handcrafted features](https://mlsanity.com/paper/2112.10307/) (2021)
- [CNNs, Transformers, Hybrid, and Vision Language Models for Skin Cancer Detection](https://mlsanity.com/paper/2605.26294/) (2026)

## Abstract (arXiv)

This paper provides the required description of the methods used to obtain submitted results for Task1 and Task 3 of ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection. The results have been created by a team of researchers at the University of Dayton Signal and Image Processing Lab. In this submission, traditional classifiers with hand-crafted features are utilized for Task 1 and Task 3. Our team is providing additional separate submissions using deep learning methods for comparison.