Abstract: Skin diseases are a growing health concern worldwide, and early detection is crucial for effective treatment and management. This paper presents a novel method for skin disease detection using image processing and machine learning techniques. The proposed system utilizes high-quality images of the skin, which are pre- processed to enhance features and reduce noise. Image processing techniques such as color normalization, edge detection, and segmentation are employed to extract relevant features from the images. These features are then used as inputs for machine learning models, specifically Convolutional Neural Networks (CNN), which have demonstrated high performance in image classification tasks. The CNN model is trained on a large dataset of labelled skin disease images to distinguish between various skin conditions, including melanoma, eczema, psoriasis, and acne. The system is designed to automate the diagnostic process, reducing the reliance on manual examination by dermatologists and minimizing the risk of human error. Performance metrics, such as accuracy, precision, recall, and F1 score, are evaluated to assess the efficiency and reliability of the system. The results demonstrate the potential of combining image processing and machine learning to provide a robust tool for early skin disease detection, offering a significant advantage in terms of speed and accessibility. The method’s ability to provide accurate, real-time analysis can greatly aid healthcare professionals in diagnosing skin diseases promptly and can be used in both clinical and remote settings, facilitating broader access to dermatological care. This approach offers promising implications for improving the accuracy and efficiency of skin disease diagnosis, ultimately contributing to better patient outcomes and more accessible healthcare solutions.
Keywords: Skin disease detection, image processing, machine learning, classification, medical imaging, Automation.
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