Skin Cancer Classification With Deep Learning: A Systematic Review

Abstract

Skin cancer is one of the most dangerous diseases in the world. Correctly classifying skin lesions at an early stage could aid clinical decision-making by providing an accurate disease diagnosis, potentially increasing the chances of cure before cancer spreads. However, achieving automatic skin cancer classification is difficult because the majority of skin disease images used for training are imbalanced and in short supply; meanwhile, the model’s cross-domain adaptability and robustness are also critical challenges. Recently, many deep learning-based methods have been widely used in skin cancer classification to solve the above issues and achieve satisfactory results. Nonetheless, reviews that include the abovementioned frontier problems in skin cancer classification are still scarce. Therefore, in this article, we provide a comprehensive overview of the latest deep learning-based algorithms for skin cancer classification. We begin with an overview of three types of dermatological images, followed by a list of publicly available datasets relating to skin cancers. After that, we review the successful applications of typical convolutional neural networks for skin cancer classification. As a highlight of this paper, we next summarize several frontier problems, including data imbalance, data limitation, domain adaptation, model robustness, and model efficiency, followed by corresponding solutions in the skin cancer classification task. Finally, by summarizing different deep learning-based methods to solve the frontier challenges in skin cancer classification, we can conclude that the general development direction of these approaches is structured, lightweight, and multimodal. Besides, for readers’ convenience, we have summarized our findings in figures and tables. Considering the growing popularity of deep learning, there are still many issues to overcome as well as chances to pursue in the future.

A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis

“…For skin cancer detection in early stages, a complete physical examination is of paramount importance; however, visual inspection is often not sufficient, and less than one quarter of U.S. patients will have a dermatologic examination in their lifetime 1 . Dermoscopy is a diagnostic tool, which allows for improved recognition of numerous skin lesions when compared to naked eye examination alone; however, this improvement depends on the level of training and experience of clinicians 2 . In recent years, advances have been made in noninvasive tools to improve skin cancer diagnostic performance, including the use of artificial intelligence (AI) for clinical and/or dermoscopic image diagnosis in dermatology.…”

MetaAttention model: a new approach for skin lesion diagnosis using AB features and attention mechanism

“…After the emergence of deep learning, these algorithms can automatically learn semantic features from large-scale datasets with higher accuracy and efficiency. As a result, deep learning-based methods such as Convolutional Neural Network (CNN), ResNet, InceptionNet, EfficientNet (which are transfer learning models that were pre-trained on a huge dataset of images) and Vision Transformer have been used to solve the great majority of skin cancer classification problems in recent years and obtained satisfactory results [26].…”

Deep learning model enhanced skin cancer detection

“…Ensemble learning has become a popular method for improving the accuracy of machine learning models in various domains. It is particularly useful in cases where the goal is to minimize error, as is often the case in CAD systems [29], [30]. Among the studies that utilized ensemble methods is that of [31] who used ensembles to combine the predictions of three different deep convolutional neural networks (DCNNs).…”

“…According to the World Health Organization (WHO), skin cancer is a major public health concern. It is the most common type of cancer worldwide, with a high incidence and prevalence in many countries [50] [2]. According to current estimates, one in five Americans will develop skin cancer at some point in their lifetime [1], [3], [4 Skin cancer can be broadly categorized into two: nonmelanoma skin cancer (NMSC) and melanoma.…”