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

Wang, Yiting; Nazir, Shah; Shafiq, Muhammad

doi:10.1155/2021/5552743

Cited by 23 publications

(16 citation statements)

References 42 publications

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Healthcare System for Quality of Service

Bordoloi

Singh

Sanober

et al. 2022

Journal of Healthcare Engineering

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Deep learning (DL) and machine learning (ML) have a pivotal role in logistic supply chain management and smart manufacturing with proven records. The ability to handle large complex data with minimal human intervention made DL and ML a success in the healthcare systems. In the present healthcare system, the implementation of ML and DL is extensive to achieve a higher quality of service and quality of health to patients, doctors, and healthcare professionals. ML and DL were found to be effective in disease diagnosis, acute disease detection, image analysis, drug discovery, drug delivery, and smart health monitoring. This work presents a state-of-the-art review on the recent advancements in ML and DL and their implementation in the healthcare systems for achieving multi-objective goals. A total of 10 papers have been thoroughly reviewed that presented novel works of ML and DL integration in the healthcare system for achieving various targets. This will help to create reference data that can be useful for future implementation of ML and DL in other sectors of healthcare system.

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Section: Introductionmentioning

confidence: 99%

Deep Learning in Healthcare System for Quality of Service

Bordoloi

Singh

Sanober

et al. 2022

Journal of Healthcare Engineering

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“…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 ].…”

Section: Introductionmentioning

confidence: 88%

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

Erciyas

Barışçı

2021

Computational and Mathematical Methods in Medicine

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Diabetic retinopathy occurs as a result of the harmful effects of diabetes on the eyes. Diabetic retinopathy is also a disease that should be diagnosed early. If not treated early, vision loss may occur. It is estimated that one third of more than half a million diabetic patients will have diabetic retinopathy by the 22nd century. Many effective methods have been proposed for disease detection with deep learning. In this study, unlike other studies, a deep learning-based method has been proposed in which diabetic retinopathy lesions are detected automatically and independently of datasets, and the detected lesions are classified. In the first stage of the proposed method, a data pool is created by collecting diabetic retinopathy data from different datasets. With Faster RCNN, lesions are detected, and the region of interests are marked. The images obtained in the second stage are classified using the transfer learning and attention mechanism. The method tested in Kaggle and MESSIDOR datasets reached 99.1% and 100% ACC and 99.9% and 100% AUC, respectively. When the obtained results are compared with other results in the literature, it is seen that more successful results are obtained.

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“…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.…”

Section: Developments Of Transfer Learning-based Fdmentioning

confidence: 99%

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

Chen¹,

Luo²,

Huang³

et al. 2022

Preprint

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<p>Over the last decade, transfer learning has attracted a great deal of attention as a new learning paradigm, based on which fault diagnosis (FD) approaches have been intensively developed to improve the safety and reliability of modern automation systems. Because of inevitable factors such as the varying work environment, performance degradation of components, and heterogeneity among similar automation systems, the FD method having long-term applicabilities becomes attractive. Motivated by these facts, transfer learning has been an indispensable tool that endows the FD methods with self-learning and adaptive abilities. On the presentation of basic knowledge in this field, a comprehensive review of transfer learning-motivated FD methods, whose two subclasses are developed based on knowledge calibration and knowledge compromise, is carried out in this survey paper. Finally, some open problems, potential research directions, and conclusions are highlighted. Different from the existing reviews of transfer learning, this survey focuses on how to utilize knowledge specifically for the FD tasks, based on which three principles and a new classification strategy of transfer learning-motivated FD techniques are also presented. We hope this work will constitute a timely contribution to transfer learning-motivated techniques regarding the FD topic.</p>

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

Cited by 23 publications

References 42 publications

Deep Learning in Healthcare System for Quality of Service

Deep Learning in Healthcare System for Quality of Service

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

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

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