Advanced Image Processing for Dermatological Disease Detection

Abstract

Skin diseases pose a significant health concern globally, particularly in regions like Saudi Arabia, where desert climates contribute to their prevalence. Despite advancements in medical technology, the cost and accessibility of diagnosing such conditions remain significant barriers. Leveraging cutting-edge image processing techniques, our research aims to address this challenge by proposing a cost-effective and efficient method for skin disease detection. Our approach harnesses the power of digital image analysis, utilizing readily available equipment such as cameras and computers. By employing a combination of image resizing and feature extraction via pre-trained convolutional neural networks, we streamline the identification process. Multiclass SVM classification further enhances accuracy, ensuring precise diagnosis across a spectrum of skin conditions. Through our innovative methodology, we have achieved a remarkable accuracy rate of 100% in detecting three distinct types of skin diseases. Our system not only identifies the type of disease but also provides insights into its spread and severity, empowering both patients and healthcare professionals with invaluable information. "SkinVision" represents a groundbreaking advancement in dermatological diagnosis, offering swift and reliable results while minimizing the burden on healthcare resources. By integrating state-of-the-art image processing and computer vision technologies, we pave the way for accessible and efficient skin disease detection in Saudi Arabia and beyond.