# Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data

arXiv: https://arxiv.org/abs/2201.10650 · HTML: https://mlsanity.com/paper/2201.10650/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 76 references that are in the map.
- First posted on arXiv 25 Jan 2022.

## Builds on (most cited first, 6 of 6)

- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection](https://mlsanity.com/paper/1703.00523/) (2017)
- [Image Classification of Melanoma, Nevus and Seborrheic Keratosis by Deep Neural Network Ensemble](https://mlsanity.com/paper/1703.03108/) (2017)
- [Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks](https://mlsanity.com/paper/1709.09780/) (2017)
- [Automatic Skin Lesion Analysis using Large-scale Dermoscopy Images and Deep Residual Networks](https://mlsanity.com/paper/1703.04197/) (2017)
- [Accessible Melanoma Detection using Smartphones and Mobile Image Analysis](https://mlsanity.com/paper/1711.09553/) (2017)

## Abstract (arXiv)

kin cancer is considered one of the most common type of cancer in several countries. Due to the difficulty and subjectivity in the clinical diagnosis of skin lesions, Computer-Aided Diagnosis systems are being developed for assist experts to perform more reliable diagnosis. The clinical analysis and diagnosis of skin lesions relies not only on the visual information but also on the context information provided by the patient. This work addresses the problem of pigmented skin lesions detection from smartphones captured images. In addition to the features extracted from images, patient context information was collected to provide a more accurate diagnosis. The experiments showed that the combination of visual features with context information improved final results. Experimental results are very promising and comparable to experts.