TESL-Net: A Transformer-Enhanced CNN for Accurate Skin Lesion Segmentation

TL;DR

TESL-Net is a hybrid deep learning model combining CNNs, Bi-ConvLSTM, and Swin transformers, designed to improve skin lesion segmentation accuracy by capturing local and long-range features, outperforming existing methods.

Contribution

Introduces TESL-Net, a novel hybrid network that integrates CNNs, Bi-ConvLSTM, and Swin transformer for enhanced skin lesion segmentation.

Findings

Achieves state-of-the-art Jaccard index on ISIC datasets.

Effectively captures contextual and long-range dependencies in dermoscopic images.

Outperforms existing U-Net and FCN-based methods.

Abstract

Early detection of skin cancer relies on precise segmentation of dermoscopic images of skin lesions. However, this task is challenging due to the irregular shape of the lesion, the lack of sharp borders, and the presence of artefacts such as marker colours and hair follicles. Recent methods for melanoma segmentation are U-Nets and fully connected networks (FCNs). As the depth of these neural network models increases, they can face issues like the vanishing gradient problem and parameter redundancy, potentially leading to a decrease in the Jaccard index of the segmentation model. In this study, we introduced a novel network named TESL-Net for the segmentation of skin lesions. The proposed TESL-Net involves a hybrid network that combines the local features of a CNN encoder-decoder architecture with long-range and temporal dependencies using bi-convolutional long-short-term memory (Bi-ConvLSTM) networks and a Swin transformer. This enables the model to account for the uncertainty of segmentation over time and capture contextual channel relationships in the data. We evaluated the efficacy of TESL-Net in three commonly used datasets (ISIC 2016, ISIC 2017, and ISIC 2018) for the segmentation of skin lesions. The proposed TESL-Net achieves state-of-the-art performance, as evidenced by a significantly elevated Jaccard index demonstrated by empirical results.