Introduction

Skin diseases affect over a billion people worldwide, yet accurate diagnosis remains a persistent challenge. The visual similarity between many dermatological conditions often leads to frequent misdiagnoses, particularly in early stages or in resource-limited settings. This diagnostic complexity underscores the need for automated, reliable tools in dermatology.

Deep learning (DL), a subset of artificial intelligence, has emerged as a transformative approach to skin disease classification, harnessing large and diverse image datasets to automate and enhance diagnostic accuracy. Recent advances leverage multi-model architectures and transfer learning techniques—such as fine-tuning Xception, EfficientNet, and DenseNet—to classify a broad range of skin conditions with performance that rivals or exceeds traditional methods. These models integrate sophisticated preprocessing, data augmentation, and interpretability strategies to improve robustness, generalizability, and clinical applicability.

By enabling earlier detection, reducing healthcare costs, and expanding access to dermatology expertise—particularly in underserved areas—DL holds the potential to reshape dermatological diagnostics. This article explores recent developments in DL-based skin disease classification, with a focus on methods, clinical applications, and future directions informed by research published from 2022 onward.1,2,3,4

Deep Learning in Medical Image Classification

Unlike traditional machine learning, which depends on manual feature extraction, deep learning automates this process through convolutional neural networks (CNNs). In dermatology, architectures such as ResNet, DenseNet, and EfficientNet excel at recognizing complex lesion patterns. Transfer learning further addresses the limitation of small annotated datasets by adapting pre-trained networks to specialized skin image collections such as the ISIC Archive.

The typical DL workflow begins with assembling diverse clinical and dermoscopic images from hospitals or public repositories. Preprocessing steps—such as color normalization, artifact removal, and hair segmentation—enhance image quality. Models are then trained using CNNs, hybrid frameworks, or emerging Vision Transformers, with performance evaluated via metrics including accuracy, sensitivity, specificity, and area under the ROC curve (AUC). Once validated, these models can be deployed within clinical decision support systems, mobile applications, or teledermatology platforms, enabling rapid, accessible, and scalable diagnostic support.5,6,7

Real-World Applications

Recent studies demonstrate that DL can achieve dermatologist-level accuracy in skin disease classification, particularly in melanoma detection. For instance, a multi-model architecture combining Xception with transfer learning has classified over 40 skin conditions with up to 95% overall accuracy, including 94% accuracy and 99.5% AUROC for melanoma—performance on par with expert dermatologists.

Mobile AI applications now enable self-assessment through smartphone imaging, using lightweight models to deliver early diagnostic insights in regions with limited dermatology services. Teledermatology platforms increasingly incorporate real-time AI classification, supporting clinicians with rapid lesion evaluation during virtual consultations and improving both diagnostic precision and patient triage.

Hybrid DL methods—such as multi-scale channel attention CNNs, fusion of EfficientNet and ResNet, and novel transfer learning approaches—have been applied successfully to common inflammatory and neoplastic skin diseases, including eczema, psoriasis, acne, and multiple skin cancers. These innovations enhance accuracy, scalability, and clinical relevance, positioning DL as a critical adjunct in modern dermatology.8,9,10,11

Reference:

  1. Badr M, Elkasaby A, Alrahmawy M, El-Metwally S. A Multi-model Deep Learning Architecture for Diagnosing Multi-class Skin Diseases. J Imaging Inform Med. 2025 Jun;38(3):1776-1795. doi: 10.1007/s10278-024-01300-w. Epub 2024 Oct 31. PMID: 39482493; PMCID: PMC12092911.

  2. Sarı MO, Keser K. Classification of skin diseases with deep learning based approaches. Sci Rep. 2025 Jul 28;15(1):27506. doi: 10.1038/s41598-025-13275-x. PMID: 40721845; PMCID: PMC12304466.

  3. Alruwaili M, Mohamed M. An Integrated Deep Learning Model with EfficientNet and ResNet for Accurate Multi-Class Skin Disease Classification. Diagnostics (Basel). 2025 Feb 25;15(5):551. doi: 10.3390/diagnostics15050551. PMID: 40075797; PMCID: PMC11898587.

  4. Liu, H., Dou, Y., Wang, K. et al. A skin disease classification model based on multi scale combined efficient channel attention module. Sci Rep 15, 6116 (2025). https://doi.org/10.1038/s41598-025-90418-0

  5. Muhammad Romail Imran, Abdul Wahab Paracha, Hamza Anjum, Haris Anjum, Muhammad Abbas, & Muhammad Fasih. (2024). SKIN DISEASE CLASSIFICATION USING DEEP LEARNING. Journal of Population Therapeutics and Clinical Pharmacology, 31(9), 2623-2643. https://doi.org/10.53555/1hthv124

  6. Srinivasu PN, SivaSai JG, Ijaz MF, Bhoi AK, Kim W, Kang JJ. Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM. Sensors (Basel). 2021 Apr 18;21(8):2852. doi: 10.3390/s21082852. PMID: 33919583; PMCID: PMC8074091.

  7. Venkatesh, K.P., Raza, M.M., Nickel, G. et al. Deep learning models across the range of skin disease. npj Digit. Med. 7, 32 (2024). https://doi.org/10.1038/s41746-024-01033-8

  8. Gulzar Y, Agarwal S, Soomro S, Kandpal M, Turaev S, Onn CW, Saini S and Bounsiar A (2025) Next-generation approach to skin disorder prediction employing hybrid deep transfer learning. Front. Big Data 8:1503883. doi: 10.3389/fdata.2025.1503883

  9. Manzoor K, Gilal NU, Agus M, Schneider J. Dual-stage segmentation and classification framework for skin lesion analysis using deep neural network. Digit Health. 2025 Jul 13;11:20552076251351858. doi: 10.1177/20552076251351858. PMID: 40666627; PMCID: PMC12260298.

  10. Yu Y, Jia H, Zhang L, Xu S, Zhu X, Wang J, Wang F, Han L, Jiang H, Zhou Q, Xin C. Deep Multi-Modal Skin-Imaging-Based Information-Switching Network for Skin Lesion Recognition. Bioengineering (Basel). 2025 Mar 12;12(3):282. doi: 10.3390/bioengineering12030282. PMID: 40150746; PMCID: PMC11939189.

  11. Wei ML, Tada M, So A and Torres R (2024) Artificial intelligence and skin cancer. Front. Med. 11:1331895. doi: 10.3389/fmed.2024.1331895