A Deep Learning Framework Empowered by the Internet of Medical Devices for Skin Cancer Detection and Classification Through Transfer Learning

This chapter presents an innovative approach to tackle the pressing need for early and precise detection of skin cancer, a disease with increasing global prevalence and potentially life-threatening consequences. The challenge of accurately identifying skin lesions is compounded by the diverse appearance of human skin and the presence of hair, demanding sophisticated machine learning (ML) techniques. Our proposed model addresses this challenge by harnessing a comprehensive dataset covering a wide spectrum of skin tones, ensuring robust representation in the training data. Leveraging state-of-the-art ML algorithms, especially convolutional neural networks (CNNs) and transfer learning, the system proficiently recognizes patterns indicative of skin cancer lesions. To enhance adaptability to skin diversity, meticulous calibration using data augmentation techniques is employed, significantly contributing to efforts to mitigate health disparities in dermatological diagnoses. With skin cancer incidence escalating globally, timely detection is paramount for effective treatment. To address this urgency, our chapter presents a deep learning framework for skin lesion classification in images, seamlessly integrating with the Internet of Medical Things (IoMT). This integration empowers the system to remotely assist medical specialists in diagnosing and treating skin cancer, extending its impact beyond traditional clinical settings. Performance evaluation metrics underscore the superiority of our framework over other pretrained architectures in terms of precision, recall, and accuracy, emphasizing the imperative of developing inclusive ML models to advance skin cancer detection across diverse populations.