# SLSNet: Skin lesion segmentation using a lightweight generative adversarial network

## TL;DR

SLSNet is a lightweight, efficient GAN-based model for skin lesion segmentation that achieves high accuracy and speed with minimal computational resources, suitable for real-time dermoscopic applications.

## Contribution

This paper introduces SLSNet, a novel lightweight GAN that combines 1-D kernels, attention modules, and multiscale features for accurate skin lesion segmentation with low resource requirements.

## Key findings

- Achieves 97.61% accuracy on ISIC 2018 dataset.
- Runs at over 110 FPS on a GTX1080Ti GPU.
- Uses only 2.35 million parameters, comparable to state-of-the-art methods.

## Abstract

The determination of precise skin lesion boundaries in dermoscopic images using automated methods faces many challenges, most importantly, the presence of hair, inconspicuous lesion edges and low contrast in dermoscopic images, and variability in the color, texture and shapes of skin lesions. Existing deep learning-based skin lesion segmentation algorithms are expensive in terms of computational time and memory. Consequently, running such segmentation algorithms requires a powerful GPU and high bandwidth memory, which are not available in dermoscopy devices. Thus, this article aims to achieve precise skin lesion segmentation with minimum resources: a lightweight, efficient generative adversarial network (GAN) model called SLSNet, which combines 1-D kernel factorized networks, position and channel attention, and multiscale aggregation mechanisms with a GAN model. The 1-D kernel factorized network reduces the computational cost of 2D filtering. The position and channel attention modules enhance the discriminative ability between the lesion and non-lesion feature representations in spatial and channel dimensions, respectively. A multiscale block is also used to aggregate the coarse-to-fine features of input skin images and reduce the effect of the artifacts. SLSNet is evaluated on two publicly available datasets: ISBI 2017 and the ISIC 2018. Although SLSNet has only 2.35 million parameters, the experimental results demonstrate that it achieves segmentation results on a par with the state-of-the-art skin lesion segmentation methods with an accuracy of 97.61%, and Dice and Jaccard similarity coefficients of 90.63% and 81.98%, respectively. SLSNet can run at more than 110 frames per second (FPS) in a single GTX1080Ti GPU, which is faster than well-known deep learning-based image segmentation models, such as FCN. Therefore, SLSNet can be used for practical dermoscopic applications.