Enhanced Defensive Model Using CNN against Adversarial Attacks for Medical Education through Human Computer Interaction

Abstract

The design of human-machine interfaces is more precise and demanding in the medical and healthcare industries. Medical monitoring equipment demands more consistent and effective interpretation, as well as fast and straightforward operation due to its monitoring and reference functions. Consequently, it is crucial to consider how people interact with computers when designing the interface for medical monitoring devices. Nowadays people are giving more importance to health than anything in the world. Therefore, as it is related to peoples’ safety, the architecture of human-computer communication must be carefully considered in the studies and development of high-end medical equipment. The price of training physicians and other medical professionals is rising dramatically. Most of the countries have stepped forward from the traditional medical teaching system to a more human computer interactive teaching and learning environment with innovative technologies. This article focuses on the related researches, existing HCI applications for healthcare and the application of deep neural network for disease classification. The proposed work is to develop a healthcare learning platform to offer healthcare education to both medical practitioners and also for common people. This can be implemented as Mobile apps using human-computer interface technology and also as a website with Artificial Intelligence and Machine Learning Techniques. This proposal’s primary goal is to provide anytime, everywhere access to healthcare education for physiological and medical teaching courses, consequently advancing national health care.