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Bioengineering
Special Issues
Application of Convolutional Neural Networks in Bioimaging and Biosignal Processes
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Application of Convolutional Neural Networks in Bioimaging and Biosignal Processes

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 1449

Special Issue Editors

Dr. Sebelan Danishvar

E-Mail Website
Guest Editor
College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK
Interests: image processing; signal processing; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Miguel Valencia

E-Mail Website
Guest Editor
Neurosciences Division, CIMA, University of Navarra, 31008 Pamplona, Navarra, Spain
Interests: bioengineering; brain diseases; neurophysiology; neurotechnology; signal analysis; ML/AI; embedded systems; complex systems

Special Issue Information

Dear Colleagues,

As is widely known, with the advancement of artificial intelligence (AI), the use of computer-aided diagnosis (CAD) systems in medicine has skyrocketed in recent years. Convolution neural network (CNN) models have demonstrated significantly high performance in identification, division, and classification, allowing them to be useful and effective in disease diagnosis and treatment. However, the majority of studies have used deep convolutional architectures with no significant changes. In this Special Issue, submissions on the following areas are of special interest: efforts to provide new architectures related to convolutional neural networks, new methods in feature selection/extraction and learning, and solving problems related to the scarcity and imbalance of medical data (including transfer learning, artificial data generation, and data augmentation). This research can include applications such as the automatic detection of sleep stages, the detection and classification of epilepsy stages, the automatic detection of driver fatigue, the detection and classification of emotions, etc., from physiological signals. In addition, research on the segmentation of medical images based on deep convolutional networks, including diagnosing and classifying tumors of the brain, liver, bone, etc., is also welcome.

Dr. Sebelan Danishvar
Dr. Miguel Valencia
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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • biomedical image/signal analysis
  • biomedical image/signal processing
  • new AI architectures
  • detection and recognition in biomedical image/signals
  • automated/computer-aided diagnosis using convolutional neural networks

Published Papers (1 paper)

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Research

14 pages, 4131 KiB  
Article
Concurrent Learning Approach for Estimation of Pelvic Tilt from Anterior–Posterior Radiograph
by Ata Jodeiri, Hadi Seyedarabi, Sebelan Danishvar, Seyyed Hossein Shafiei, Jafar Ganjpour Sales, Moein Khoori, Shakiba Rahimi and Seyed Mohammad Javad Mortazavi
Bioengineering 2024, 11(2), 194; https://doi.org/10.3390/bioengineering11020194 - 17 Feb 2024
Viewed by 1000
Abstract
Accurate and reliable estimation of the pelvic tilt is one of the essential pre-planning factors for total hip arthroplasty to prevent common post-operative complications such as implant impingement and dislocation. Inspired by the latest advances in deep learning-based systems, our focus in this [...] Read more.
Accurate and reliable estimation of the pelvic tilt is one of the essential pre-planning factors for total hip arthroplasty to prevent common post-operative complications such as implant impingement and dislocation. Inspired by the latest advances in deep learning-based systems, our focus in this paper has been to present an innovative and accurate method for estimating the functional pelvic tilt (PT) from a standing anterior–posterior (AP) radiography image. We introduce an encoder–decoder-style network based on a concurrent learning approach called VGG-UNET (VGG embedded in U-NET), where a deep fully convolutional network known as VGG is embedded at the encoder part of an image segmentation network, i.e., U-NET. In the bottleneck of the VGG-UNET, in addition to the decoder path, we use another path utilizing light-weight convolutional and fully connected layers to combine all extracted feature maps from the final convolution layer of VGG and thus regress PT. In the test phase, we exclude the decoder path and consider only a single target task i.e., PT estimation. The absolute errors obtained using VGG-UNET, VGG, and Mask R-CNN are 3.04 ± 2.49, 3.92 ± 2.92, and 4.97 ± 3.87, respectively. It is observed that the VGG-UNET leads to a more accurate prediction with a lower standard deviation (STD). Our experimental results demonstrate that the proposed multi-task network leads to a significantly improved performance compared to the best-reported results based on cascaded networks. Full article
(This article belongs to the Special Issue Application of Convolutional Neural Networks in Bioimaging and Biosignal Processes)
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