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Deep Neural Networks for Smart Healthcare Systems

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 9105

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Special Issue Editors

Dr. Dragos D. Taralunga

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Guest Editor

Department of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, 061071 Bucharest, Romania
Interests: application of artificial intelligence in medicine, biomedical signal processing (EEG, ECG, EMG, EOG, EHG, fECG, PPG, PCG etc), mHealth; Blockchain for medical applications

Dr. Bogdan C. Florea

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Guest Editor

Department of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, 061071 Bucharest, Romania
Interests: blockchain; control systems; automation; automatic control; medical engineering; artificial intelligence

Dr. Anamaria Radoi

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Guest Editor

Department of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, 061071 Bucharest, Romania
Interests: artificial intelligence; remote sensing; time series analysis; multimodal data analysis

Prof. Dr. G. Mihaela Neagu

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Guest Editor

Department of Applied Electronics and Information Engineering, University Politehnica of Bucharest, 060042 Bucharest, Romania
Interests: brain computer interface; artificial intelligence for medical diagnosis; biomedical signal and image processing; fetal monitoring
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Special Issue Information

Dear Colleagues,

Chronic and complex illnesses have an important economic impact on national healthcare systems. The advancement of wearable technology and medical sensors has led to the collection of a significant amount of medical and healthcare data for individual patients; however, artificial intelligence and, more specifically, deep learning approaches allow the building of smart healthcare systems. In view of reducing the elevated costs associated with disease treatment and hospitalization, early and improved diagnosis can be achieved by taking into consideration medical data recorded with various types of medical sensors (biomedical images, biomedical signals and parameters). The interpretation of this heterogeneous data and intelligent human–computer interaction systems will generate better prevention and diminished costs in cases of chronical and complex illnesses.

The present Special Issue will address the application of deep neural networks in the field of (semi)automatic disease diagnosis. It welcomes original contributions that focus on novel deep learning approaches and digital signal/imaging processing that can be used to extract and classify relevant diagnostic information from multimodal and/or multichannel medical and healthcare data. Reviews focused on the latest achievements of scientific research and emerging techniques are also welcome.

Possible topics include but are not limited to deep neural network approaches for automatic diagnosis and the classification of neurodegenerative diseases, arrythmias, COVID-19, pulmonary diseases, fetal wellbeing and septicemia, as well as smart healthcare systems.

Dr. Dragos D. Taralunga
Dr. Bogdan C. Florea
Dr. Anamaria Radoi
Prof. Dr. G. Mihaela Neagu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

deep neural network
deep learning
medical sensors
biomedical images
biomedical signals
disease diagnosis
smart healthcare

Published Papers (3 papers)

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Research

18 pages, 3147 KiB

Open AccessArticle

Artificial Intelligence-Driven Eye Disease Classification Model

by Abdul Rahaman Wahab Sait

Appl. Sci. 2023, 13(20), 11437; https://doi.org/10.3390/app132011437 - 18 Oct 2023

Cited by 1 | Viewed by 3784

Abstract

Eye diseases can result in various challenges and visual impairments. These diseases can affect an individual’s quality of life and general health and well-being. The symptoms of eye diseases vary widely depending on the nature and severity of the disease. Early diagnosis can protect individuals from visual impairment. Artificial intelligence (AI)-based eye disease classification (EDC) assists physicians in providing effective patient services. However, the complexities of the fundus image affect the classifier’s performance. There is a demand for a practical EDC for identifying eye diseases in the earlier stages. Thus, the author intends to build an EDC model using the deep learning (DL) technique. Denoising autoencoders are used to remove the noises and artifacts from the fundus images. The single-shot detection (SSD) approach generates the key features. The whale optimization algorithm (WOA) with Levy Flight and Wavelet search strategy is followed for selecting the features. In addition, the Adam optimizer (AO) is applied to fine-tune the ShuffleNet V2 model to classify the fundus images. Two benchmark datasets, ocular disease intelligent recognition (ODIR) and EDC datasets, are utilized for performance evaluation. The proposed EDC model achieved accuracy and Kappa values of 99.1 and 96.4, and 99.4 and 96.5, in the ODIR and EDC datasets, respectively. It outperformed the recent EDC models. The findings highlight the significance of the proposed EDC model in classifying eye diseases using complex fundus images. Healthcare centers can implement the proposed model to improve their standards and serve a more significant number of patients. In the future, the proposed model can be extended to identify a comprehensive range of eye diseases. Full article

(This article belongs to the Special Issue Deep Neural Networks for Smart Healthcare Systems)

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32 pages, 3141 KiB

Open AccessArticle

Biomac3D: 2D-to-3D Human Pose Analysis Model for Tele-Rehabilitation Based on Pareto Optimized Deep-Learning Architecture

by Rytis Maskeliūnas, Audrius Kulikajevas, Robertas Damaševičius, Julius Griškevičius and Aušra Adomavičienė

Appl. Sci. 2023, 13(2), 1116; https://doi.org/10.3390/app13021116 - 13 Jan 2023

Cited by 3 | Viewed by 3183

Abstract

The research introduces a unique deep-learning-based technique for remote rehabilitative analysis of image-captured human movements and postures. We present a ploninomial Pareto-optimized deep-learning architecture for processing inverse kinematics for sorting out and rearranging human skeleton joints generated by RGB-based two-dimensional (2D) skeleton recognition algorithms, with the goal of producing a full 3D model as a final result. The suggested method extracts the entire humanoid character motion curve, which is then connected to a three-dimensional (3D) mesh for real-time preview. Our method maintains high joint mapping accuracy with smooth motion frames while ensuring anthropometric regularity, producing a mean average precision (mAP) of

0.950

for the task of predicting the joint position of a single subject. Furthermore, the suggested system, trained on the MoVi dataset, enables a seamless evaluation of posture in a 3D environment, allowing participants to be examined from numerous perspectives using a single recorded camera feed. The results of evaluation on our own self-collected dataset of human posture videos and cross-validation on the benchmark MPII and KIMORE datasets are presented. Full article

(This article belongs to the Special Issue Deep Neural Networks for Smart Healthcare Systems)

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16 pages, 2063 KiB

Open AccessArticle

Tokens Shuffling Approach for Privacy, Security, and Reliability in IoHT under a Pandemic

by Nour Bahbouh, Abdullah Basahel, Sandra Sendra and Adnan Ahmed Abi Sen

Appl. Sci. 2023, 13(1), 114; https://doi.org/10.3390/app13010114 - 22 Dec 2022

Cited by 13 | Viewed by 1367

Abstract

Privacy and security are unavoidable challenges in the future of smart health services and systems. Several approaches for preserving privacy have been provided in the Internet of Health Things (IoHT) applications. However, with the emergence of COVID-19, the healthcare centers needed to track, collect, and share more critical data such as the location of those infected and monitor social distancing. Unfortunately, the traditional privacy-preserving approaches failed to deal effectively with emergency circumstances. In the proposed research, we introduce a Tokens Shuffling Approach (TSA) to preserve collected data’s privacy, security, and reliability during the pandemic without the need to trust a third party or service providers. TSA depends on a smartphone application and the proposed protocol to collect and share data reliably and safely. TSA depends on a proposed algorithm for swapping the identities temporarily between cooperated users and then hiding the identities by employing fog nodes. The fog node manages the cooperation process between users in a specific area to improve the system’s performance. Finally, TSA uses blockchain to save data reliability, ensure data integrity, and facilitate access. The results prove that TSA performed better than traditional approaches regarding data privacy and the performance level. Further, we noticed that it adapted better during emergency circumstances. Moreover, TSA did not affect the accuracy of the collected data or its related statistics. On the contrary, TSA will not affect the quality of primary healthcare services. Full article

(This article belongs to the Special Issue Deep Neural Networks for Smart Healthcare Systems)

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