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Electronics
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Deep Learning Technology for Biomedical Signals and Images Applications
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Deep Learning Technology for Biomedical Signals and Images Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: 25 January 2025 | Viewed by 6345

Special Issue Editors

Prof. Dr. Shing-Hong Liu

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan
Interests: electronic circuits; development of medical instruments; cardiovascular measurement system; deep learning; machine learning; biomedical signal process; development of embedded systems in health care
Special Issues, Collections and Topics in MDPI journals
Dr. Chiun-Li Chin

E-Mail Website
Guest Editor
Department of Automatic Control Engineering, Feng Chia University, Seatwen Taichung City 40724, Taiwan
Interests: image and video processing; pattern recognition; deep learning; artificial neural network; medical signal processing; healthcare; fuzzy theory

Special Issue Information

Dear Colleagues,

Recent advances in health informatics, artificial intelligence, and sensing techniques have generated increasing interest from both industry and academia. Deep learning technology, which has made significant progress in various fields such as natural language recognition (e.g., ChatGTP), has further fueled this interest. As such, the Special Issue titled "Deep Learning Technology for Biomedical Signals and Images Applications" will bring together researchers and experts to present and discuss the latest developments and technical solutions related to advances in deep learning for signal and image processing of bioelectronic devices. This Special Issue will feature original, unpublished articles focused on theoretical analysis, biomedical signal and image processing, novel system architecture construction and design, experimental studies, and wearable device development.

This Special Issue will focus on (but is not limited to) the following topics:

  • Biomedical Signal Processing;
  • Biomedical Imaging and Image Processing;
  • Bioinformatics and Computational Biology, Systems Biology and Modeling Methodologies;
  • Cardiovascular and Respiratory Systems Engineering;
  • Neural and Rehabilitation Engineering;
  • Therapeutic and Diagnostic Systems, Devices and Technologies and Clinical Engineering;
  • Healthcare Information Systems and Telemedicine;
  • Biomedical Engineering Education;
  • Technologies for Active Ageing and Wellbeing.

Prof. Dr. Shing-Hong Liu
Dr. Chiun-Li Chin
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. Electronics 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 learning technology
  • bioelectronic device
  • signal and image process
  • wearable device

Published Papers (7 papers)

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Research

19 pages, 2826 KiB  
Article
Automated Left Ventricle Segmentation in Echocardiography Using YOLO: A Deep Learning Approach for Enhanced Cardiac Function Assessment
by Madankumar Balasubramani, Chih-Wei Sung, Mu-Yang Hsieh, Edward Pei-Chuan Huang, Jiann-Shing Shieh and Maysam F. Abbod
Electronics 2024, 13(13), 2587; https://doi.org/10.3390/electronics13132587 - 1 Jul 2024
Viewed by 560
Abstract
Accurate segmentation of the left ventricle (LV) using echocardiogram (Echo) images is essential for cardiovascular analysis. Conventional techniques are labor-intensive and exhibit inter-observer variability. Deep learning has emerged as a powerful tool for automated medical image segmentation, offering advantages in speed and potentially [...] Read more.
Accurate segmentation of the left ventricle (LV) using echocardiogram (Echo) images is essential for cardiovascular analysis. Conventional techniques are labor-intensive and exhibit inter-observer variability. Deep learning has emerged as a powerful tool for automated medical image segmentation, offering advantages in speed and potentially superior accuracy. This study explores the efficacy of employing a YOLO (You Only Look Once) segmentation model for automated LV segmentation in Echo images. YOLO, a cutting-edge object detection model, achieves exceptional speed–accuracy balance through its well-designed architecture. It utilizes efficient dilated convolutional layers and bottleneck blocks for feature extraction while incorporating innovations like path aggregation and spatial attention mechanisms. These attributes make YOLO a compelling candidate for adaptation to LV segmentation in Echo images. We posit that by fine-tuning a pre-trained YOLO-based model on a well-annotated Echo image dataset, we can leverage the model’s strengths in real-time processing and precise object localization to achieve robust LV segmentation. The proposed approach entails fine-tuning a pre-trained YOLO model on a rigorously labeled Echo image dataset. Model performance has been evaluated using established metrics such as mean Average Precision (mAP) at an Intersection over Union (IoU) threshold of 50% (mAP50) with 98.31% and across a range of IoU thresholds from 50% to 95% (mAP50:95) with 75.27%. Successful implementation of YOLO for LV segmentation has the potential to significantly expedite and standardize Echo image analysis. This advancement could translate to improved clinical decision-making and enhanced patient care. Full article
(This article belongs to the Special Issue Deep Learning Technology for Biomedical Signals and Images Applications)
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17 pages, 11048 KiB  
Article
An Automated Assessment Method for Chronic Kidney Disease–Mineral and Bone Disorder (CKD-MBD) Utilizing Metacarpal Cortical Percentage
by Ming-Jui Wu, Shao-Chun Tseng, Yan-Chin Gau and Wei-Siang Ciou
Electronics 2024, 13(12), 2389; https://doi.org/10.3390/electronics13122389 - 18 Jun 2024
Viewed by 470
Abstract
Chronic kidney disease–mineral and bone disorder (CKD-MBD) frequently occurs in hemodialysis patients and is a common cause of osteoporosis. Regular dual-energy X-ray absorptiometry (DXA) scans are used to monitor these patients, but frequent, cost-effective, and low-dose alternatives are needed. This study proposes an [...] Read more.
Chronic kidney disease–mineral and bone disorder (CKD-MBD) frequently occurs in hemodialysis patients and is a common cause of osteoporosis. Regular dual-energy X-ray absorptiometry (DXA) scans are used to monitor these patients, but frequent, cost-effective, and low-dose alternatives are needed. This study proposes an automatic CKD-MBD assessment model using histogram equalization and a squeeze-and-excitation block-based residual U-Net (SER-U-Net) with hand diagnostic radiography for preliminary classification. The process involves enhancing image contrast with histogram equalization, extracting features with the SE-ResNet model, and segmenting metacarpal bones using U-Net. Ultimately, a correlation analysis is carried out between the calculated dual metacarpal cortical percentage (dMCP) and DXA T-scores. The model’s performance was validated by analyzing clinical data from 30 individuals, achieving a 93.33% accuracy in classifying bone density compared to DXA results. This automated method provides a rapid, effective tool for CKD-MBD assessment in clinical settings. Full article
(This article belongs to the Special Issue Deep Learning Technology for Biomedical Signals and Images Applications)
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18 pages, 4303 KiB  
Article
LMA-EEGNet: A Lightweight Multi-Attention Network for Neonatal Seizure Detection Using EEG signals
by Weicheng Zhou, Wei Zheng, Youbing Feng and Xiaolong Li
Electronics 2024, 13(12), 2354; https://doi.org/10.3390/electronics13122354 - 16 Jun 2024
Viewed by 435
Abstract
Neonatal epilepsy is an early postnatal brain disorder, and automatic seizure detection is crucial for timely diagnosis and treatment to reduce potential brain damage. This work proposes a novel Lightweight Multi-Attention Network, LMA-EEGNet, for diagnosing neonatal epileptic seizures from multi-channel EEG signals employing [...] Read more.
Neonatal epilepsy is an early postnatal brain disorder, and automatic seizure detection is crucial for timely diagnosis and treatment to reduce potential brain damage. This work proposes a novel Lightweight Multi-Attention Network, LMA-EEGNet, for diagnosing neonatal epileptic seizures from multi-channel EEG signals employing dilated depthwise separable convolution (DDS Conv) for feature extraction and using pointwise convolution followed by global average pooling for classification. The proposed approach substantially reduces the model size, number of parameters, and computational complexity, which are crucial for real-time detection and clinical diagnosis of neonatal epileptic seizures. LMA-EEGNet integrates temporal and spectral features through distinct temporal and spectral branches. The temporal branch uses DDS Conv to extract temporal features, enhanced by a channel attention mechanism. The spectral branch utilizes similar convolutions alongside a spatial attention mechanism to highlight key frequency components. Outputs from both branches are merged and processed through a pointwise convolution layer and a global average pooling layer for efficient neonatal seizure detection. Experimental results show that our model, with only 2471 parameters and a size of 23 KB, achieves an accuracy of 95.71% and an AUC of 0.9862, demonstrating its potential for practical deployment. This study provides an effective deep learning solution for the early detection of neonatal epileptic seizures, improving diagnostic accuracy and timeliness. Full article
(This article belongs to the Special Issue Deep Learning Technology for Biomedical Signals and Images Applications)
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14 pages, 3905 KiB  
Article
Classification of Microscopic Hyperspectral Images of Blood Cells Based on Lightweight Convolutional Neural Network
by Jinghui Fang
Electronics 2024, 13(8), 1578; https://doi.org/10.3390/electronics13081578 - 20 Apr 2024
Viewed by 866
Abstract
Hyperspectral imaging has emerged as a novel imaging modality in the medical field, offering the ability to acquire images of biological tissues while simultaneously providing biochemical insights for in-depth tissue analysis. This approach facilitates early disease diagnosis, presenting advantages over traditional medical imaging [...] Read more.
Hyperspectral imaging has emerged as a novel imaging modality in the medical field, offering the ability to acquire images of biological tissues while simultaneously providing biochemical insights for in-depth tissue analysis. This approach facilitates early disease diagnosis, presenting advantages over traditional medical imaging techniques. Addressing challenges such as the computational burden of existing convolutional neural networks (CNNs) and imbalances in sample data, this paper introduces a lightweight GhostMRNet for the classification of microscopic hyperspectral images of human blood cells. The proposed model employs Ghost Modules to replace conventional convolutional layers and a cascading approach with small convolutional kernels for multiscale feature extraction, aiming to enhance feature extraction capabilities while reducing computational complexity. Additionally, an SE (Squeeze-and-Excitation) module is introduced to selectively allocate weights to features in each channel, emphasizing informative features and efficiently achieving spatial–spectral feature extraction in microscopic hyperspectral imaging. We evaluated the performance of the proposed GhostMRNet and compared it with other state-of-the-art models using two real medical hyperspectral image datasets. The experimental results demonstrate that GhostMRNet exhibits a superior performance, with an overall accuracy (OA), average accuracy (AA), and Kappa coefficient reaching 99.965%, 99.565%, and 0.9925, respectively. In conclusion, the proposed GhostMRNet achieves a superior classification performance at a smaller computational cost, thereby providing a novel approach for blood cell detection. Full article
(This article belongs to the Special Issue Deep Learning Technology for Biomedical Signals and Images Applications)
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21 pages, 9712 KiB  
Article
Renal Pathological Image Classification Based on Contrastive and Transfer Learning
by Xinkai Liu, Xin Zhu, Xingjian Tian, Tsuyoshi Iwasaki, Atsuya Sato and Junichiro James Kazama
Electronics 2024, 13(7), 1403; https://doi.org/10.3390/electronics13071403 - 8 Apr 2024
Viewed by 747
Abstract
Following recent advancements in medical laboratory technology, the analysis of high-resolution renal pathological images has become increasingly important in the diagnosis and prognosis prediction of chronic nephritis. In particular, deep learning has been widely applied to computer-aided diagnosis, with an increasing number of [...] Read more.
Following recent advancements in medical laboratory technology, the analysis of high-resolution renal pathological images has become increasingly important in the diagnosis and prognosis prediction of chronic nephritis. In particular, deep learning has been widely applied to computer-aided diagnosis, with an increasing number of models being used for the analysis of renal pathological images. The diversity of renal pathological images and the imbalance between data acquisition and annotation have placed a significant burden on pathologists trying to perform reliable and timely analysis. Transfer learning based on contrastive pretraining is emerging as a viable solution to this dilemma. By incorporating unlabeled positive pretraining images and a small number of labeled target images, a transfer learning model is proposed for high-accuracy renal pathological image classification tasks. The pretraining dataset used in this study includes 5000 mouse kidney pathological images from the Open TG-GATEs pathological image dataset (produced by the Toxicogenomics Informatics Project of the National Institutes of Biomedical Innovation, Health, and Nutrition in Japan). The transfer training dataset comprises 313 human immunoglobulin A (IgA) chronic nephritis images collected at Fukushima Medical University Hospital. The self-supervised contrastive learning algorithm “Bootstrap Your Own Latent” was adopted for pretraining a residual-network (ResNet)-50 backbone network to extract glomerulus feature expressions from the mouse kidney pathological images. The self-supervised pretrained weights were then used for transfer training on the labeled images of human IgA chronic nephritis pathology, culminating in a binary classification model for supervised learning. In four cross-validation experiments, the proposed model achieved an average classification accuracy of 92.2%, surpassing the 86.8% accuracy of the original RenNet-50 model. In conclusion, this approach successfully applied transfer learning through mouse renal pathological images to achieve high classification performance with human IgA renal pathological images. Full article
(This article belongs to the Special Issue Deep Learning Technology for Biomedical Signals and Images Applications)
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15 pages, 3151 KiB  
Article
Effects of the Hyperparameters on CNNs for MDD Classification Using Resting-State EEG
by Chia-Yen Yang and Hsin-Min Lee
Electronics 2024, 13(1), 186; https://doi.org/10.3390/electronics13010186 - 31 Dec 2023
Viewed by 1003
Abstract
To monitor patients with depression, objective diagnostic tools that apply biosignals and exhibit high repeatability and efficiency should be developed. Although different models can help automatically learn discriminative features, inappropriate adoption of input forms and network structures may cause performance degradation. Accordingly, the [...] Read more.
To monitor patients with depression, objective diagnostic tools that apply biosignals and exhibit high repeatability and efficiency should be developed. Although different models can help automatically learn discriminative features, inappropriate adoption of input forms and network structures may cause performance degradation. Accordingly, the aim of this study was to systematically evaluate the effects of convolutional neural network (CNN) architectures when using two common electroencephalography (EEG) inputs on the classification of major depressive disorder (MDD). EEG data for 21 patients with MDD and 21 healthy controls were obtained from an open-source database. Five hyperparameters (i.e., number of convolutional layers, filter size, pooling type, hidden size, and batch size) were then evaluated. Finally, Grad-CAM and saliency map were applied to visualize the trained models. When raw EEG signals were employed, optimal performance and efficiency were achieved as more convolutional layers and max pooling were used. Furthermore, when mixed features were employed, a larger hidden layer and smaller batch size were optimal. Compared with other complex networks, this configuration involves a relatively small number of layers and less training time but a relatively high accuracy. Thus, high accuracy (>99%) can be achieved in MDD classification by using an appropriate combination in a simple model. Full article
(This article belongs to the Special Issue Deep Learning Technology for Biomedical Signals and Images Applications)
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15 pages, 7334 KiB  
Article
A Novel Fuzzy DBNet for Medical Image Segmentation
by Chiun-Li Chin, Jun-Cheng Lin, Chieh-Yu Li, Tzu-Yu Sun, Ting Chen, Yan-Ming Lai, Pei-Chen Huang, Sheng-Wen Chang and Alok Kumar Sharma
Electronics 2023, 12(12), 2658; https://doi.org/10.3390/electronics12122658 - 13 Jun 2023
Cited by 2 | Viewed by 1499
Abstract
When doctors are fatigued, they often make diagnostic errors. Similarly, pharmacists may also make mistakes in dispensing medication. Therefore, object segmentation plays a vital role in many healthcare-related areas, such as symptom analysis in biomedical imaging and drug classification. However, many traditional deep-learning [...] Read more.
When doctors are fatigued, they often make diagnostic errors. Similarly, pharmacists may also make mistakes in dispensing medication. Therefore, object segmentation plays a vital role in many healthcare-related areas, such as symptom analysis in biomedical imaging and drug classification. However, many traditional deep-learning algorithms use a single view of an image for segmentation or classification. When the image is blurry or incomplete, these algorithms fail to segment the pathological area or the shape of the drugs accurately, which can then affect subsequent treatment plans. Consequently, we propose the Fuzzy DBNet, which combines the dual butterfly network and the fuzzy ASPP in a deep-learning network and processes images from both sides of an object simultaneously. Our experiments used multi-category pill and lung X-ray datasets for training. The average Dice coefficient of our proposed model reached 95.05% in multi-pill segmentation and 97.05% in lung segmentation. The results showed that our proposed model outperformed other state-of-the-art networks in both applications, demonstrating that our model can use multiple views of an image to obtain image segmentation or identification. Full article
(This article belongs to the Special Issue Deep Learning Technology for Biomedical Signals and Images Applications)
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