Assist-Dermo: A Lightweight Separable Vision Transformer Model for Multiclass Skin Lesion Classification

Abstract

A dermatologist-like automatic classification system is developed in this paper to recognize nine different classes of pigmented skin lesions (PSLs), using a separable vision transformer (SVT) technique to assist clinical experts in early skin cancer detection. In the past, researchers have developed a few systems to recognize nine classes of PSLs. However, they often require enormous computations to achieve high performance, which is burdensome to deploy on resource-constrained devices. In this paper, a new approach to designing SVT architecture is developed based on SqueezeNet and depthwise separable CNN models. The primary goal is to find a deep learning architecture with few parameters that has comparable accuracy to state-of-the-art (SOTA) architectures. This paper modifies the SqueezeNet design for improved runtime performance by utilizing depthwise separable convolutions rather than simple conventional units. To develop this Assist-Dermo system, a data augmentation technique is applied to control the PSL imbalance problem. Next, a pre-processing step is integrated to select the most dominant region and then enhance the lesion patterns in a perceptual-oriented color space. Afterwards, the Assist-Dermo system is designed to improve efficacy and performance with several layers and multiple filter sizes but fewer filters and parameters. For the training and evaluation of Assist-Dermo models, a set of PSL images is collected from different online data sources such as Ph2, ISBI-2017, HAM10000, and ISIC to recognize nine classes of PSLs. On the chosen dataset, it achieves an accuracy (ACC) of 95.6%, a sensitivity (SE) of 96.7%, a specificity (SP) of 95%, and an area under the curve (AUC) of 0.95. The experimental results show that the suggested Assist-Dermo technique outperformed SOTA algorithms when recognizing nine classes of PSLs. The Assist-Dermo system performed better than other competitive systems and can support dermatologists in the diagnosis of a wide variety of PSLs through dermoscopy. The Assist-Dermo model code is freely available on GitHub for the scientific community.

Dermo-Seg: ResNet-UNet Architecture and Hybrid Loss Function for Detection of Differential Patterns to Diagnose Pigmented Skin Lesions

“…Consequently, a computer-aided diagnostic (CAD) system is necessary to help dermatologists in analyzing these attributes. Numerous diagnostic techniques, including CAD systems, have been proposed in the literature for skin lesion segmentation [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. However, many of these techniques primarily focus on boundary segmentation [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ,…”

“…Numerous diagnostic techniques, including CAD systems, have been proposed in the literature for skin lesion segmentation . However, many of these techniques primarily focus on boundary segmentation while giving less attention to the segmentation of actual attributes within the lesion [29][30][31][32][33][34][35][36][37][38][39][40][41][42]. The purpose of employing CAD systems is to automatically identify irregularities or skin disorders present within the lesion.…”

“…However, there are only a few existing methods discussed in the literature on attribute segmentation in skin lesions [29][30][31][32][33][34][35][36][37][38][39][40][41][42]. Current skin lesion segmentation methods typically follow a traditional approach, involving the detection of the lesion area in the image, the extraction of discriminative features from the lesion, and the subsequent classification of the skin lesion type.…”

“…Hence, there are challenges in obtaining further data for assessment and in efficiently utilizing existing data. Currently, there are very few deep learning-based approaches [30][31][32][33][34][35][36][37][38][39][40] employed for the segmentation of given lesion structures, such as in Task 2 of the ISIC 2018 Challenge [52].…”

Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review

“…They include metadata related to clinical diagnosis, lesion type, and body location, among other factors. Their open access and the diversity of data they offer make them highly valuable to the scientific community, in dermatological research, and in the development of artificial intelligence tools for the diagnosis of skin diseases as a significant complement to expert diagnosis [87][88][89][90][91]. Additionally, the PH2 dataset consists of a recompilation of 200 images, focusing on a local objective rather than being broadly applicable to other case studies [69,74].…”