Skin Disease Detection and Classification of Actinic Keratosis and Psoriasis Utilizing Deep Transfer Learning

• URL: http://arxiv.org/abs/2501.13713v1
• Date: Thu, 23 Jan 2025 14:43:53 GMT
• Title: Skin Disease Detection and Classification of Actinic Keratosis and Psoriasis Utilizing Deep Transfer Learning
• Authors: Fahud Ahmmed, Md. Zaheer Raihan, Kamnur Nahar, D. M. Asadujjaman, Md. Mahfujur Rahman, Abdullah Tamim,
• Abstract summary: Skin diseases can arise from infections, allergies, genetic factors, autoimmune disorders, hormonal imbalances, or environmental triggers such as sun damage and pollution. We propose a novel and efficient method for diagnosing skin diseases using deep learning techniques.
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• Abstract: Skin diseases can arise from infections, allergies, genetic factors, autoimmune disorders, hormonal imbalances, or environmental triggers such as sun damage and pollution. Some skin diseases, such as Actinic Keratosis and Psoriasis, can be fatal if not treated in time. Early identification is crucial, but the diagnostic methods for these conditions are often expensive and not widely accessible. In this study, we propose a novel and efficient method for diagnosing skin diseases using deep learning techniques. This approach employs a modified VGG16 Convolutional Neural Network (CNN) model. The model includes several convolutional layers and utilizes ImageNet weights with modified top layers. The top layer is updated with fully connected layers and a final softmax activation layer to classify skin diseases. The dataset used, titled "Skin Disease Dataset," is publicly available. While the VGG16 architecture does not include data augmentation by default, preprocessing techniques such as rotation, shifting, and zooming were applied to augment the data prior to model training. The proposed methodology achieved 90.67% accuracy using the modified VGG16 model, demonstrating its reliability in classifying skin diseases. The promising results highlight the potential of this approach for real-world applications.

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