Classification of Various Skin Diseases by Using Deep Learning

Abstract

Skin conditions are frequently misidentified, causing continued discomfort for individuals. This study introduces a sophisticated deep learning technique that leverages convolutional neural networks (CNNs) for the classification of skin diseases. It involves the utilization of pre-trained DenseNet201, ResNet152V2, and VGG16 models on skin images to achieve this goal. Data augmentation was employed to enhance the resilience of the model and mitigate overfitting. The performance of the models was assessed with metrics such as accuracy, precision, recall, F1 score, and Cohen’s kappa, all indicating encouraging outcomes for clinical application. The study also delves into model interpretability, illustrating the models’ capability to accurately forecast novel, unseen instances. This method has the potential to improve the precision of diagnoses, enabling healthcare professionals to better differentiate skin conditions, thereby minimizing misdiagnoses and promoting the long-term comfort of patients.