SkinNet: Enhancing Dermatological Diagnosis Through a New Deep Learning Framework

Skin cancer is a prevalent and potentially deadly disease, with early detection and treatment crucial for improved outcomes. With technological advances, the Internet of Things (IoT) in telemedicine has become increasingly popular, enabling remote diagnosis and treatment of diseases. In this paper, we investigate the feasibility of using IoT for skin cancer prediction and diagnosis in telemedicine. We propose a system that uses a combination of IoT devices, deep learning algorithms, and medical imaging techniques to predict the presence of skin cancer. The system comprises a smartphone with a high-resolution camera, a dermoscopy, and a cloud-based machine learning model. The smartphone camera captures images of the skin lesions, while the dermoscopy provides magnified images of the lesion’s surface for further analysis. The algorithm analyzes various features of the lesion, such as color, texture, and shape, to predict the likelihood of malignancy. The results are then transmitted to the cloud-based machine learning model, which further analyzes the data and provides a diagnosis. We evaluated the performance of our system using a dataset of skin cancer images, and our results show that the proposed system achieves a high accuracy rate of over 97,6% in predicting skin cancer. Additionally, the system provides a fast and efficient way to diagnose skin cancer, allowing for early detection and timely treatment, which can significantly improve patient outcomes.