Skin diseases are common health conditions that affect people worldwide. Early detection and accurate classification of skin diseases are important for supporting timely medical treatment. However, identifying skin diseases through visual examination can be challenging because different skin conditions may have similar visual characteristics. Recent advancements in artificial intelligence and deep learning have created opportunities to develop computer-aided systems for medical image analysis.

This research focuses on human skin disease detection using deep learning techniques. The proposed approach uses skin images as input and applies image preprocessing techniques to prepare the images for classification. Convolutional Neural Networks (CNNs) can automatically extract important features such as colour, texture, and lesion patterns. Residual Networks (ResNet) can also be used to learn complex image features through their residual connections, supporting the classification of different skin conditions. The system aims to identify patterns in skin images and classify them into predefined disease categories based on the training data.

The performance of the proposed approach can be assessed using evaluation metrics such as accuracy, precision, recall, and F1-score.

 

This research explores the potential of deep learning to support preliminary skin disease screening and improve the efficiency of image-based analysis. The system is intended to assist healthcare professionals and is not a replacement for professional medical diagnosis

Keywords— Keywords: Human Skin Disease Detection, Deep Learning, Convolutional Neural Network (CNN), ResNet, Image Processing, Skin Lesion Classification, Melanoma Detection, Medical Image Analysis, Transfer Learning, Artificial Intelligence.