An Overview on Analyzing Deep Learning and Transfer Learning Approaches for Health Monitoring

Abstract

With the rise and advancement of technology, early detection and involvement in health-associated monitoring through home control are growing with population aging. The expansion of healthy life expectations is progressively significant due to the speedy aging of the world population. The patient requires early and home-based treatment to detect and prevent disease on time and with less effort. Home-based health monitoring has been considered the need of a smart home. The services of health monitoring can facilitate the patient by collecting and analyzing the data of health for tackling diverse complex issues of health at a large scale. Health monitoring is a sustainable progression of clinical trials for ensuring that health is monitored according to the defined protocol and standard operating procedures. Various scenarios can be considered for monitoring health and are performed through experts of the field. Healthcare systems are having large-scale infrastructure of electronic devices, medical information systems, wearable and smart devices, medical records, and handheld devices. The growth in medical infrastructure, combined with the development of computational approaches in healthcare, has empowered practitioners and researchers to devise a novel solution in the innovative spectra. A detailed report of the existing literature in terms of deep learning and transfer learning is the dire need and facilitating of modern healthcare. To overcome these limitations, therefore, the proposed study presents a comprehensive review of the existing approaches, techniques, and methods associated with deep learning and transfer learning for health monitoring. This review will help researchers to formulate new ideas for facilitating healthcare based on the existing evidence.

Deep Learning in Healthcare System for Quality of Service

“…Health data are useful in many ways when processed intelligently [ 4 , 5 ]. The intelligent processing of health data may help to prevent pandemic outbreaks in a city, states, or countries, helping to identify acute diseases such as Alzheimer's disease, diabetes, cardiovascular diseases, and lung cancer [ 6 ]. Blockchain is more reliable and robust as once the data are created, it can only be read and not edited or updated.…”

An Effective Method for Detecting and Classifying Diabetic Retinopathy Lesions Based on Deep Learning

“…Experts believe that deep learning will facilitate medical studies in the coming years of medicine. The successes obtained in the works [ 23 – 30 ] on the subject support this idea; it is about the improvement, classification, segmentation, and detection of medical images and related to the images and taking vital precautions. Moreover, Limwattanayingyong et al showed that DL was more successful when they compared sight-threatening DR (STDR) screening with educated human grading and DL grading [ 31 ].…”

Transfer Learning-motivated Intelligent Fault Diagnosis Designs: A Survey, Insights, and Perspectives

“…Up to now, there are some articles dedicated to presenting the state of the art of transfer learning-motivated FD methods, such as the survey papers [60], [94], [95]. Retrospecting the development of transfer learning-motivated FD approaches, it is not difficult to find an intimate relationship between deep learning and transfer learning.…”