Abstract: Dermatological diseases rate has been increasing for past few decades. Most of these diseases tend to pass on from one person to another and are also based on visual perspectives, the dermatological diseases of one kind found on one part of the body might look different on another part of the body and diseases of different kinds on one part might look similar on other body parts. Skin diseases pose significant challenges in early detection and diagnosis, requiring accurate and efficient methods to improve patient outcomes. This report presents a novel Skin Disease Detection System that integrates Gabor filters and the deep learning models. The system leverages Gabor filters to extract texture features from skin lesion images, capturing unique patterns associated with various skin diseases. These features are concatenated and fed into a fine-tuned VGG16 model, which extracts high-level features and used CNN for predicting the type of skin disease. The system's workflow encompasses image preprocessing using Gabor filter, feature extraction using VGG16 model and disease classification using the CNN model, we predict four type of disease (Acne, Benign, Dermatitis, Eczema). By combining texture analysis and deep learning techniques, the system aims to enhance accuracy and assist dermatologists in efficiently screening and skin diseases. The proposed system has the potential to revolutionize skin disease, facilitating early intervention and improved patient care.

Keywords: Gabor filter, Deep learning algorithm – Convolutional neural network (CNN) and Visual geometry group 16(VGG16).

| DOI: 10.17148/IARJSET.2023.10569