Deep Learning Models for Automated Classification of Seborrheic Keratosis: A Comprehensive Literature Review and Comparative Study

Seborrheic keratosis (SK) is a common benign skin lesion that is often clinically mistaken for malignant melanoma due to visual similarities. This misdiagnosis can lead to unnecessary patient anxiety, invasive procedures, and increased healthcare costs. Deep learning models have recently shown promise in improving the accuracy and objectivity of skin lesion diagnosis. In this work, we present a comprehensive literature review of automated SK classification using deep learning, and we perform a comparative study of four state-of-the-art architectures: ResNet-34, EfficientNet-B1, Vision Transformer (ViT), and VGG16, on multi-source dermoscopic image datasets. Our literature review highlights that, while most prior studies focused on melanoma detection or general skin lesion classification, relatively few have specifically addressed SK, which is a significant gap given its prevalence and propensity for misdiagnosis. We summarize key contributions from recent research, including convolutional neural network (CNN) approaches and emerging transformer-based models for skin lesion analysis. In our experimental evaluation across three diverse datasets (DermoFit, BCN20000, and an Argentine clinical dataset), we found that individual model performance varies widely. ResNet-34 achieved a high area under the ROC curve (AUC) of 0.9742 with strong specificity, and EfficientNet-B1 attained the highest validation accuracy (94.41%) among the CNNs. A Vision Transformer model, after careful tuning and augmentation, outperformed the CNNs, achieving a test accuracy of 97.28% on the SK classification task. This improved ViT model demonstrated a balanced sensitivity and specificity (both above 95%), underscoring the potential of transformer architectures in skin lesion classification. We discuss these results in the context of existing literature and clinical requirements. Overall, our study provides an up-to-date review of deep learning techniques for SK identification and emphasizes the value of transformer-based models for this challenging dermatological problem.