Decoding skin cancer classification: perspectives, insights, and advances through researchers' lens

Abstract

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.

Keywords: Deep learning; Machine learning; Medical image; Skin cancer; Skin lesion classification.

Fig. 1

Types of skin cancer.

Fig. 2

Sample images of various types of skin cancer.

Fig. 3

(a) Bar graph illustrating the distribution of research papers published over the past 18 years that leverage ML, DL, hybrid, and multimodal approaches. Note that this is not an exhaustive collection, but a subset selected for this survey. (b) Pie chart illustrating the percentage distribution of papers based on ML, DL, hybrid, and multimodal approaches.

Fig. 4

Taxonomy of different skin cancer classification strategies used in this survey.

Fig. 5

(a) Flowchart of the skin cancer classification system proposed by Waheed et al.; (b) Structure of the hybrid GA-ANN classifier proposed by Aswin et al..

Fig. 6

(a) Pie chart illustrating the percentage distribution of papers based on different DL models. (b) Pie chart illustrating the percentage distribution of papers based on various CNN-based techniques.

Fig. 7

Architecture of the SkinNet-8 model proposed by Fahad et al..

Fig. 8

Overview of the soft attention unit proposed by Datta et al..

Fig. 9

(a) Block diagram of the GAN-based model proposed by Bisla et al.; (b) Architecture of the DCGAN proposed by Bisla et al..

Fig. 10

Overview of the hybrid model proposed by Tembhurne et al..