A survey, review, and future trends of skin lesion segmentation and classification

Abstract

The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a user-friendly tool to dermatologists to reduce the challenges encountered or associated with manual inspection. This article aims to provide a comprehensive literature survey and review of a total of 594 publications (356 for skin lesion segmentation and 238 for skin lesion classification) published between 2011 and 2022. These articles are analyzed and summarized in a number of different ways to contribute vital information regarding the methods for the development of CAD systems. These ways include relevant and essential definitions and theories, input data (dataset utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria. We intend to investigate a variety of performance-enhancing approaches, including ensemble and post-processing. We also discuss these dimensions to reveal their current trends based on utilization frequencies. In addition, we highlight the primary difficulties associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these difficulties. Findings, recommendations, and trends are disclosed to inform future research on developing an automated and robust CAD system for skin lesion analysis.

This manuscript has been accepted to be published in Computers in Biology and Medicine and has a total of 106 pages (single column and double spacing), 13 figures, and 11 tables

References in corpus (27)

• Domain-adversarial neural networks to address the appearance variability of histopathology images
• Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
• Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification
• A Survey on Deep Learning for Skin Lesion Segmentation
• Skin Lesion Classification Using CNNs with Patch-Based Attention and Diagnosis-Guided Loss Weighting
• MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation
• Bi-directional Dermoscopic Feature Learning and Multi-scale Consistent Decision Fusion for Skin Lesion Segmentation
• Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition
• Skin Lesion Classification Using Deep Multi-scale Convolutional Neural Networks
• Identifying Melanoma Images using EfficientNet Ensemble: Winning Solution to the SIIM-ISIC Melanoma Classification Challenge
• Deep Learning for Skin Lesion Classification
• Automatic Skin Lesion Segmentation using Semi-supervised Learning Technique
• Skin Lesion Classification using Class Activation Map
• DONet: Dual Objective Networks for Skin Lesion Segmentation
• Handling Inter-Annotator Agreement for Automated Skin Lesion Segmentation
• Self-supervised Assisted Active Learning for Skin Lesion Segmentation
• Transfer Learning for Melanoma Detection: Participation in ISIC 2017 Skin Lesion Classification Challenge
• A Smartphone based Application for Skin Cancer Classification Using Deep Learning with Clinical Images and Lesion Information
• MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and Classification
• k-Means Clustering and Ensemble of Regressions: An Algorithm for the ISIC 2017 Skin Lesion Segmentation Challenge
• Global and Local Information Based Deep Network for Skin Lesion Segmentation
• Skin lesion segmentation and classification using deep learning and handcrafted features
• U-Net-based Models for Skin Lesion Segmentation: More Attention and Augmentation
• Lesion Net -- Skin Lesion Segmentation Using Coordinate Convolution and Deep Residual Units
• Properties Of Winning Tickets On Skin Lesion Classification
• Frequency and Spatial domain based Saliency for Pigmented Skin Lesion Segmentation
• Comparative Analysis of Automatic Skin Lesion Segmentation with Two Different Implementations

Cited by in corpus (3)

• A Region of Interest Focused Triple UNet Architecture for Skin Lesion Segmentation
• Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distribution
• Deep Neural Networks Fused with Textures for Image Classification