mlsanityData to 7 Oct 2026

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

arXiv 2208.12232 ↗First posted 25 Aug 2022eess.IV cs.CV cs.LGcited by 20

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 48 of its 546 references that are in the map. First posted on arXiv 25 Aug 2022.

Builds on

48 of 546 references.

References · 48

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  3. 2017Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)cited by 549
  4. 2020A Patient-Centric Dataset of Images and Metadata for Identifying Melanomas Using Clinical Contextcited by 146
  5. 2019Interpretations are useful: penalizing explanations to align neural networks with prior knowledgecited by 144
  6. 2021Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyondcited by 75
  7. 2019Skin Lesion Classification Using Ensembles of Multi-Resolution EfficientNets with Meta Datacited by 43
  8. 2021Boundary-aware Transformers for Skin Lesion Segmentationcited by 39
  9. 2019Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classificationcited by 33
  10. 2019A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classificationcited by 32
  11. 2016Deep Learning Ensembles for Melanoma Recognition in Dermoscopy Imagescited by 26
  12. 2021Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classificationcited by 23
36 more
  1. 2019DSNet: Automatic Dermoscopic Skin Lesion Segmentationcited by 22
  2. 2019Deep neural network or dermatologist?cited by 20
  3. 2020Melanoma Detection using Adversarial Training and Deep Transfer Learningcited by 17
  4. 2020Bi-directional Dermoscopic Feature Learning and Multi-scale Consistent Decision Fusion for Skin Lesion Segmentationcited by 15
  5. 2017Skin Lesion Classification Using Hybrid Deep Neural Networkscited by 14
  6. 2020The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classificationcited by 14
  7. 2018A Multi-stage Framework with Context Information Fusion Structure for Skin Lesion Segmentationcited by 12
  8. 2021Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognitioncited by 12
  9. 2022SSD-KD: A Self-supervised Diverse Knowledge Distillation Method for Lightweight Skin Lesion Classification Using Dermoscopic Imagescited by 11
  10. 2019Skin Lesion Classification Using CNNs with Patch-Based Attention and Diagnosis-Guided Loss Weightingcited by 10
  11. 2022MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentationcited by 10
  12. 2018Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classificationcited by 9
  13. 2018Diagnostic Accuracy of Content Based Dermatoscopic Image Retrieval with Deep Classification Featurescited by 8
  14. 2017Skin lesion detection based on an ensemble of deep convolutional neural networkcited by 7
  15. 2018Automatic skin lesion segmentation on dermoscopic images by the means of superpixel mergingcited by 6
  16. 2020Investigating and Exploiting Image Resolution for Transfer Learning-based Skin Lesion Classificationcited by 6
  17. 2017Skin Lesion Segmentation: U-Nets versus Clusteringcited by 5
  18. 2019Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNetcited by 5
  19. 2020Matthews Correlation Coefficient Loss for Deep Convolutional Networks: Application to Skin Lesion Segmentationcited by 5
  20. 2018SkinNet: A Deep Learning Framework for Skin Lesion Segmentationcited by 4
  21. 2021Dermo-DOCTOR: A framework for concurrent skin lesion detection and recognition using a deep convolutional neural network with end-to-end dual encoderscited by 4
  22. 2018A Multi-task Framework for Skin Lesion Detection and Segmentationcited by 3
  23. 2019Deep Convolutional Encoder-Decoders with Aggregated Multi-Resolution Skip Connections for Skin Lesion Segmentationcited by 3
  24. 2019Brain Tissue Segmentation Using NeuroNet With Different Pre-processing Techniquescited by 3
  25. 2020Automatic Lesion Detection System (ALDS) for Skin Cancer Classification Using SVM and Neural Classifierscited by 3
  26. 2020Detector-SegMentor Network for Skin Lesion Localization and Segmentationcited by 3
  27. 2020Leveraging Adaptive Color Augmentation in Convolutional Neural Networks for Deep Skin Lesion Segmentationcited by 3
  28. 2018Webly Supervised Learning for Skin Lesion Classificationcited by 2
  29. 2018The Effects of Image Pre- and Post-Processing, Wavelet Decomposition, and Local Binary Patterns on U-Nets for Skin Lesion Segmentationcited by 2
  30. 2019TopoResNet: A hybrid deep learning architecture and its application to skin lesion classificationcited by 2
  31. 2019Automating Vitiligo Skin Lesion Segmentation Using Convolutional Neural Networkscited by 2
  32. 2020Cascaded Context Enhancement Network for Automatic Skin Lesion Segmentationcited by 2
  33. 2020CS-AF: A Cost-sensitive Multi-classifier Active Fusion Framework for Skin Lesion Classificationcited by 2
  34. 2018Skin Disease Classification versus Skin Lesion Characterization: Achieving Robust Diagnosis using Multi-label Deep Neural Networkscited by 1
  35. 2021Automated skin lesion segmentation using multi-scale feature extraction scheme and dual-attention mechanismcited by 1
  36. 2022Salient Skin Lesion Segmentation via Dilated Scale-Wise Feature Fusion Networkcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 7; 2024: 4; 2025: 7; 2026: 2.

20 papers.

Sort:

By area:

  1. 2023ECL: Class-Enhancement Contrastive Learning for Long-tailed Skin Lesion Classificationcited by 7
  2. 2023DermSynth3D: Synthesis of in-the-wild Annotated Dermatology Imagescited by 4
  3. 2026MambaLiteUNet: Cross-Gated Adaptive Feature Fusion for Robust Skin Lesion Segmentationcited by 4
  4. 2023USL-Net: Uncertainty Self-Learning Network for Unsupervised Skin Lesion Segmentationcited by 2
  5. 2024A Novel Perspective for Multi-modal Multi-label Skin Lesion Classificationcited by 2
  6. 2025Skin Lesion Phenotyping via Nested Multi-modal Contrastive Learningcited by 2
  7. 2025What Can We Learn from Inter-Annotator Variability in Skin Lesion Segmentation?cited by 2
  8. 2023Deep Neural Networks Fused with Textures for Image Classificationcited by 1
  9. 2023Only Positive Cases: 5-fold High-order Attention Interaction Model for Skin Segmentation Derived Classificationcited by 1
  10. 2024Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distributioncited by 1
  11. 2025An analysis of data variation and bias in image-based dermatological datasets for machine learning classificationcited by 1
  12. 2023Ugly Ducklings or Swans: A Tiered Quadruplet Network with Patient-Specific Mining for Improved Skin Lesion Classificationcited by 0
8 more
  1. 2023A Region of Interest Focused Triple UNet Architecture for Skin Lesion Segmentationcited by 0
  2. 2024ResNet101 and DAE for Enhance Quality and Classification Accuracy in Skin Cancer Imagingcited by 0
  3. 2024When Mamba Meets xLSTM: An Efficient and Precise Method with the xLSTM-VMUNet Model for Skin lesion Segmentationcited by 0
  4. 2025Toward Accessible Dermatology: Skin Lesion Classification Using Deep Learning Models on Mobile-Acquired Imagescited by 0
  5. 2025An End-to-End Deep Learning Framework for Arsenicosis Diagnosis Using Mobile-Captured Skin Imagescited by 0
  6. 2025DermAI: Clinical dermatology acquisition through quality-driven image collection for AI classification in mobilecited by 0
  7. 2025Implementation of a Skin Lesion Detection System for Managing Children with Atopic Dermatitis Based on Ensemble Learningcited by 0
  8. 2026DerMAE: Improving skin lesion classification through conditioned latent diffusion and MAE distillationcited by 0