Automatic Skin Lesion Analysis using Large-scale Dermoscopy Images and Deep Residual Networks

TL;DR

This paper presents an automatic skin lesion analysis method using large-scale dermoscopy images and deep residual networks, aiming to improve early melanoma detection accuracy.

Contribution

It introduces the application of deep ResNets to dermoscopy image analysis, enhancing feature learning for melanoma classification.

Findings

ResNets achieved high accuracy in melanoma detection

Improved robustness over traditional methods

Effective handling of diverse lesion characteristics

Abstract

Malignant melanoma has one of the most rapidly increasing incidences in the world and has a considerable mortality rate. Early diagnosis is particularly important since melanoma can be cured with prompt excision. Dermoscopy images play an important role in the non-invasive early detection of melanoma [1]. However, melanoma detection using human vision alone can be subjective, inaccurate and poorly reproducible even among experienced dermatologists. This is attributed to the challenges in interpreting images with diverse characteristics including lesions of varying sizes and shapes, lesions that may have fuzzy boundaries, different skin colors and the presence of hair [2]. Therefore, the automatic analysis of dermoscopy images is a valuable aid for clinical decision making and for image-based diagnosis to identify diseases such as melanoma [1-4]. Deep residual networks (ResNets) has achieved state-of-the-art results in image classification and detection related problems [5-8]. In this ISIC 2017 skin lesion analysis challenge [9], we propose to exploit the deep ResNets for robust visual features learning and representations.