Smart healthcare systems are gaining popularity with the rapid development of intelligent
sensors, the Internet of Things (IoT) applications and services, and wireless …

The increasing availability of healthcare data requires accurate analysis of disease
diagnosis, progression, and real-time monitoring to provide improved treatments to the …

Machine learning techniques in medical imaging systems are accurate, but minor
perturbations in the data known as adversarial attacks can fool them. These attacks make …

Deep learning models are widely used in healthcare systems. However, deep learning
models are vulnerable to attacks themselves. Significantly, due to the black-box nature of the …

Abstract In the Internet of Medical Things (IoMT), collaboration among institutes can help
complex medical and clinical analysis of disease. Deep neural networks (DNN) require …

In the past years, deep neural networks (DNN) have become popular in many disciplines
such as computer vision (CV), natural language processing (NLP), etc. The evolution of …

Adversarial attacks pose a significant threat to deep learning models, specifically medical
images, as they can mislead models into making inaccurate predictions by introducing …

Telemedicine applications have been recently evolved to allow patients in underdeveloped
areas to receive medical services. Meanwhile, machine learning (ML) techniques have been …

Although deep learning systems trained on medical images have shown state-of-the-art
performance in many clinical prediction tasks, recent studies demonstrate that these systems …

Convolutional Neural Networks (CNNs) have advanced existing medical systems for
automatic disease diagnosis. However, a threat to these systems arises that adversarial …