Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review

Abstract

Objective: Skin diseases constitute a widespread health concern, and the application of machine learning and deep learning algorithms has been instrumental in improving diagnostic accuracy and treatment effectiveness. This paper aims to provide a comprehensive review of the existing research on the utilization of machine learning and deep learning in the field of skin disease diagnosis, with a particular focus on recent widely used methods of deep learning. The present challenges and constraints were also analyzed and possible solutions were proposed. Methods: We collected comprehensive works from the literature, sourced from distinguished databases including IEEE, Springer, Web of Science, and PubMed, with a particular emphasis on the most recent 5-year advancements. From the extensive corpus of available research, twenty-nine articles relevant to the segmentation of dermatological images and forty-five articles about the classification of dermatological images were incorporated into this review. These articles were systematically categorized into two classes based on the computational algorithms utilized: traditional machine learning algorithms and deep learning algorithms. An in-depth comparative analysis was carried out, based on the employed methodologies and their corresponding outcomes. Conclusions: Present outcomes of research highlight the enhanced effectiveness of deep learning methods over traditional machine learning techniques in the field of dermatological diagnosis. Nevertheless, there remains significant scope for improvement, especially in improving the accuracy of algorithms. The challenges associated with the availability of diverse datasets, the generalizability of segmentation and classification models, and the interpretability of models also continue to be pressing issues. Moreover, the focus of future research should be appropriately shifted. A significant amount of existing research is primarily focused on melanoma, and consequently there is a need to broaden the field of pigmented dermatology research in the future. These insights not only emphasize the potential of deep learning in dermatological diagnosis but also highlight directions that should be focused on.

SkinSwinViT: A Lightweight Transformer-Based Method for Multiclass Skin Lesion Classification with Enhanced Generalization Capabilities

“…Pre-training and fine-tuning strategies are additionally employed to refine the performance of skin lesion identification tasks. (4) Comparative experiments against state-of-the-art methodologies corroborate the superior performance of our proposed method in multiclass skin lesion classification. The experimental results demonstrate substantial enhancements in accuracy, precision, specificity, and F1 index, confirming the effectiveness of our method.…”

“…Furthermore, three optimization strategies, namely Adam, AdamW, and SGD, are employed, with the optimal strategy selected to update SkinSwinViT parameters during training iterations. Concluding specifications entail an epoch count set to 100, with batch size values explored within the range [4,8,16,32], with superior performance observed when the batch size is set to 4 and a default learning rate of 0.00001.…”

“…With continual advancements in medical technology, there is burgeoning recognition of artificial intelligence and machine learning's potential in skin lesion identification and diagnosis [4]. Particularly in the realm of skin cancer detection, the intricate and diverse nature of skin ailments poses challenges to traditional diagnostic modalities grounded in subjective clinical assessment, heightening the propensity for misdiagnosis.…”

Artificial Intelligence: A Snapshot of Its Application in Chronic Inflammatory and Autoimmune Skin Diseases

“…Understanding the immune mechanisms underlying these dermatological conditions is crucial for developing effective diagnostic and therapeutic strategies. Moreover, advancements in the field of artificial intelligence (AI) have shown promise in enhancing the diagnosis, management, and assessment of immuno-correlated dermatological pathologies [2]. This intersection of dermatology and Life 2024, 14, 516 2 of 17 immunology plays a pivotal role in comprehending and addressing complex skin disorders with immune system involvement.…”

“…ML methods remain valuable in situations with limited data. These approaches find application in computer-aided diagnosis (CAD) systems, delivering precise classifications for dermatologists and aiding non-dermatologists in minimizing errors due to limited expertise [2]. The paper explores the evolution and achievements of ML and DL methods in diagnosis; discusses segmentation and the classification of medical images; and reviews existing challenges in immunological-related skin diseases.…”