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

Skin diseases can arise from infections, allergies, genetic factors, autoimmune disorders, hormonal imbalances, or environmental triggers such as sun damage and pollution. Skin diseases such as Actinic Keratosis and Psoriasis can be fatal. These are treatable if identified early. However, its diagnostic methods are expensive and not widely accessible. In this study, a novel and efficient method for diagnosing skin diseases using deep learning techniques has been proposed. This approach employs multiple modified Convolutional Neural Network (CNN) models like DenseNet169, DenseNet201 and VGG16. Soft voting ensemble strategy is applied to combine the strengths of individual models to get better result. These models include several convolutional layers. The models have been employed using ImageNet weights and modified top layers. The top layers are modified by fully connected layers and a final softmax activation layer to obtain the result. The dataset analyzed is publicly available and titled “Skin Disease Dataset”. The CNN architecture does not include augmentation by default; data augmentation is typically performed during preprocessing prior to model training. The proposed methodology achieved 93.11% accuracy using the ensemble strategy, demonstrating reliability in classifying skin diseases. The modified pre-trained models showed promising results, increasing its potential for real-world applications.