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Sensors
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Biomedical Signal Processing in Health Monitoring
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Biomedical Signal Processing in Health Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 25329

Special Issue Editors

Prof. Dr. Paweł Pławiak

E-Mail Website
Guest Editor
Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland
Interests: machine learning; ensemble learning; deep learning; evolutionary computation; data science; biomedical informatics; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Mohamed Hammad

E-Mail Website
Guest Editor
Faculty of Computers and Information, Menoufia University‬, Shebin El-Koom 32511, Egypt
Interests: biometrics; pattern recognition; deep learning; machine learning; AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical signal processing has become an important tool in monitoring health status and diagnosing diseases. This Special Issue aims to cover the latest advances in the field of biomedical signal processing for health monitoring, including topics such as signal acquisition, signal processing, feature extraction, and classification techniques.

We invite submissions of original research articles, review papers, and short communications related to the theme of this Special Issue. We welcome contributions from researchers in academia and industry who are working on biomedical signal processing and its application in health monitoring.

Manuscripts will undergo a rigorous peer-review process to ensure the quality of the publications.

Some possible keywords related to Biomedical Signal Processing in Health Monitoring are as follows:

  • Biomedical signal processing;
  • Health monitoring;
  • Wearable sensors;
  • Digital health;
  • Signal acquisition;
  • Signal processing;
  • Feature extraction;
  • Classification techniques;
  • Machine learning;
  • Deep learning;
  • Neural network;s
  • Electrocardiogram (ECG);
  • Electroencephalogram (EEG);
  • Photoplethysmogram (PPG);
  • Respiratory signals;
  • Blood pressure monitoring;
  • Health data analytics;
  • Remote monitoring;
  • Telemedicine.

Prof. Dr. Paweł Pławiak
Dr. Mohamed Hammad
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. Sensors 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 2600 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.

Published Papers (1 paper)

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Research

17 pages, 4163 KiB  
Article
Enhanced Deep Learning Approach for Accurate Eczema and Psoriasis Skin Detection
by Mohamed Hammad, Paweł Pławiak, Mohammed ElAffendi, Ahmed A. Abd El-Latif and Asmaa A. Abdel Latif
Sensors 2023, 23(16), 7295; https://doi.org/10.3390/s23167295 - 21 Aug 2023
Cited by 3 | Viewed by 24688
Abstract
This study presents an enhanced deep learning approach for the accurate detection of eczema and psoriasis skin conditions. Eczema and psoriasis are significant public health concerns that profoundly impact individuals’ quality of life. Early detection and diagnosis play a crucial role in improving [...] Read more.
This study presents an enhanced deep learning approach for the accurate detection of eczema and psoriasis skin conditions. Eczema and psoriasis are significant public health concerns that profoundly impact individuals’ quality of life. Early detection and diagnosis play a crucial role in improving treatment outcomes and reducing healthcare costs. Leveraging the potential of deep learning techniques, our proposed model, named “Derma Care,” addresses challenges faced by previous methods, including limited datasets and the need for the simultaneous detection of multiple skin diseases. We extensively evaluated “Derma Care” using a large and diverse dataset of skin images. Our approach achieves remarkable results with an accuracy of 96.20%, precision of 96%, recall of 95.70%, and F1-score of 95.80%. These outcomes outperform existing state-of-the-art methods, underscoring the effectiveness of our novel deep learning approach. Furthermore, our model demonstrates the capability to detect multiple skin diseases simultaneously, enhancing the efficiency and accuracy of dermatological diagnosis. To facilitate practical usage, we present a user-friendly mobile phone application based on our model. The findings of this study hold significant implications for dermatological diagnosis and the early detection of skin diseases, contributing to improved healthcare outcomes for individuals affected by eczema and psoriasis. Full article
(This article belongs to the Special Issue Biomedical Signal Processing in Health Monitoring)
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