IoT-Driven Image Processing Framework for Accurate Skin Diseases Diagnosis and Classification

Abstract

Skin diseases affect millions worldwide, and accurate diagnosis and classification are critical for effective treatment. The Internet of Things (IoT) has emerged as a powerful technology that can be used to improve healthcare systems in recent years. This paper proposes an image processing framework based on IoT for accurate skin disease diagnosis and classification. To enable remote and real-time skin disease diagnosis, the proposed framework combines the capabilities of IoT devices, such as smartphones or wearable cameras, with advanced image processing techniques. The framework uses the cameras built into IoT devices to capture high-resolution images of the affected skin areas. After that, the images are securely transmitted to a central server or cloud-based platform for processing and analysis. The framework's image-processing component employs cutting-edge algorithms for image enhancement, feature extraction, and classification. Deep learning techniques, such as convolutional neural networks (CNNs), automatically extract relevant features from skin images. These characteristics are then used to classify skin diseases accurately. The framework incorporates robust encryption and authentication mechanisms during data transmission and storage to ensure the privacy and security of sensitive medical data. Patient consent and data anonymization techniques are also used to address privacy concerns. The proposed framework has several advantages over traditional methods for diagnosing skin diseases. It enables remote diagnosis by leveraging IoT devices, reducing the need for patients to travel to healthcare facilities. The real-time analysis allows for prompt intervention and treatment planning. Extensive experiments are carried out using a diverse dataset of skin disease images to assess the performance of the proposed framework. The findings show that the framework is highly accurate in diagnosing and classifying various skin diseases.