Ray Amartya, Sarkar Sujan, Schwenker Friedhelm, Sarkar Ram
Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India.
Institute of Neural Information Processing, Ulm University, 89081, Ulm, Germany.
Sci Rep. 2024 Dec 18;14(1):30542. doi: 10.1038/s41598-024-81961-3.
Skin cancer is a significant global health concern, with timely and accurate diagnosis playing a critical role in improving patient outcomes. In recent years, computer-aided diagnosis systems have emerged as powerful tools for automated skin cancer classification, revolutionizing the field of dermatology. This survey analyzes 107 research papers published over the last 18 years, providing a thorough evaluation of advancements in classification techniques, with a focus on the growing integration of computer vision and artificial intelligence (AI) in enhancing diagnostic accuracy and reliability. The paper begins by presenting an overview of the fundamental concepts of skin cancer, addressing underlying challenges in accurate classification, and highlighting the limitations of traditional diagnostic methods. Extensive examination is devoted to a range of datasets, including the HAM10000 and the ISIC archive, among others, commonly employed by researchers. The exploration then delves into machine learning techniques coupled with handcrafted features, emphasizing their inherent limitations. Subsequent sections provide a comprehensive investigation into deep learning-based approaches, encompassing convolutional neural networks, transfer learning, attention mechanisms, ensemble techniques, generative adversarial networks, vision transformers, and segmentation-guided classification strategies, detailing various architectures, tailored for skin lesion analysis. The survey also sheds light on the various hybrid and multimodal techniques employed for classification. By critically analyzing each approach and highlighting its limitations, this survey provides researchers with valuable insights into the latest advancements, trends, and gaps in skin cancer classification. Moreover, it offers clinicians practical knowledge on the integration of AI tools to enhance diagnostic decision-making processes. This comprehensive analysis aims to bridge the gap between research and clinical practice, serving as a guide for the AI community to further advance the state-of-the-art in skin cancer classification systems.
皮肤癌是一个重大的全球健康问题，及时准确的诊断对改善患者预后起着关键作用。近年来，计算机辅助诊断系统已成为皮肤癌自动分类的强大工具，彻底改变了皮肤病学领域。本综述分析了过去18年发表的107篇研究论文，对分类技术的进展进行了全面评估，重点关注计算机视觉和人工智能（AI）在提高诊断准确性和可靠性方面日益增加的整合。本文首先概述了皮肤癌的基本概念，阐述了准确分类中存在的潜在挑战，并强调了传统诊断方法的局限性。广泛的研究涉及一系列数据集，包括研究人员常用的HAM10000和ISIC存档等。接着探讨了结合手工特征的机器学习技术，强调了其固有的局限性。随后的章节对基于深度学习的方法进行了全面研究，包括卷积神经网络、迁移学习、注意力机制、集成技术、生成对抗网络、视觉Transformer和分割引导分类策略，详细介绍了为皮肤病变分析量身定制的各种架构。该综述还揭示了用于分类的各种混合和多模态技术。通过批判性地分析每种方法并突出其局限性，本综述为研究人员提供了关于皮肤癌分类最新进展、趋势和差距的宝贵见解。此外，它为临床医生提供了关于整合人工智能工具以增强诊断决策过程的实用知识。这一全面分析旨在弥合研究与临床实践之间的差距，为人工智能社区进一步推动皮肤癌分类系统的技术发展提供指导。
