The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classification

TL;DR

This study investigates how skin lesion segmentation masks influence dermatoscopic image classification, revealing that segmentation can improve or degrade performance depending on the scenario, with automatic masks performing comparably to manual ones.

Contribution

The paper provides a comprehensive analysis of the impact of segmentation masks on skin lesion classification, highlighting scenarios where segmentation helps or hinders performance.

Findings

Segmentation masks do not significantly improve melanoma classification.

Using segmentation masks for dilated cropping enhances seborrheic keratosis classification.

Removing background info with masks degrades overall classification performance.

Abstract

Malignant melanoma (MM) is one of the deadliest types of skin cancer. Analysing dermatoscopic images plays an important role in the early detection of MM and other pigmented skin lesions. Among different computer-based methods, deep learning-based approaches and in particular convolutional neural networks have shown excellent classification and segmentation performances for dermatoscopic skin lesion images. These models can be trained end-to-end without requiring any hand-crafted features. However, the effect of using lesion segmentation information on classification performance has remained an open question. In this study, we explicitly investigated the impact of using skin lesion segmentation masks on the performance of dermatoscopic image classification. To do this, first, we developed a baseline classifier as the reference model without using any segmentation masks. Then, we used either manually or automatically created segmentation masks in both training and test phases in different scenarios and investigated the classification performances. Evaluated on the ISIC 2017 challenge dataset which contained two binary classification tasks (i.e. MM vs. all and seborrheic keratosis (SK) vs. all) and based on the derived area under the receiver operating characteristic curve scores, we observed four main outcomes. Our results show that 1) using segmentation masks did not significantly improve the MM classification performance in any scenario, 2) in one of the scenarios (using segmentation masks for dilated cropping), SK classification performance was significantly improved, 3) removing all background information by the segmentation masks significantly degraded the overall classification performance, and 4) in case of using the appropriate scenario (using segmentation for dilated cropping), there is no significant difference of using manually or automatically created segmentation masks.