DigiScope: IoT-enhanced deep learning for skin cancer prognosis

Abstract

In dermatology, early identification and intervention are crucial for optimizing patient outcomes in skin cancer care. Recent technological advances, particularly in the internet of things (IoT), have led to significant growth in telemedicine. This study introduces a cutting-edge system that proactively predicts the emergence of skin cancer by combining deep learning algorithms, IoT devices, and sophisticated medical imaging techniques. The experimental setup leverages a high-resolution mobile camera for dermoscopy, associated with a cloud-integrated machine learning framework. The proposed algorithm comprehensively examines lesion characteristics, Utilizing color, texture, and shape characteristics to evaluate the probability of malignancy. Subsequently, a cloud-hosted machine learning model analyzes and scrutinizes the collected data, yielding a thorough diagnostic evaluation. Initial results reveal that this system achieves an impressive predictive accuracy rate exceeding 97.6%, enabling swift and efficient skin cancer detection. These promising findings emphasize the potential for rapid, efficient, and proactive diagnosis, significantly improving patient prognosis and reinforcing the value of telemedicine in contemporary healthcare.