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

TL;DR

This paper presents a computer-aided diagnosis system for pigmented skin lesions that combines visual features from smartphone images with patient context data, significantly improving diagnostic accuracy.

Contribution

It introduces a novel approach integrating clinical image features with patient data for skin lesion diagnosis, enhancing reliability over image-only methods.

Findings

Combining visual and context features improves diagnosis accuracy.

Results are comparable to expert dermatologists.

The approach is effective with smartphone-captured images.

Abstract

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.