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Deep Learning Technology for Biomedical Signals and Images Applications

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

Deadline for manuscript submissions: closed (25 January 2025) | Viewed by 14026

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Prof. Dr. Shing-Hong Liu

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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
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Dr. Chiun-Li Chin

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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 This Special Issue will feature original, unpublished articles focused on theoretical analysis, biomedical signal and image processing, novel system architecture construction and design, experimental studies, and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

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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

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Published Papers (9 papers)

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Research

15 pages, 1394 KiB

Open AccessArticle

MWRD (Mamba Wavelet Reverse Diffusion)—An Efficient Fundus Image Enhancement Network Based on an Improved State-Space Model

by Tianle Chen, Yu Li and Xuedong Zhang

Electronics 2024, 13(20), 4025; https://doi.org/10.3390/electronics13204025 - 13 Oct 2024

Viewed by 1187

Abstract

This study introduces an efficient fundus image enhancement framework based on an improved Mamba model and the Denoising Diffusion Probabilistic Model (DDPM). By integrating wavelet transform for local feature extraction and applying a reverse diffusion process, this approach significantly improves the effectiveness and efficiency of enhancing low-quality fundus images. The model achieves high-precision enhancement of retinal vessel details while substantially reducing the number of parameters. Comprehensive tests on publicly available datasets show that the suggested approach surpasses various advanced low-light image enhancement methods in both quantitative and qualitative assessments. Full article

(This article belongs to the Special Issue Deep Learning Technology for Biomedical Signals and Images Applications)

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18 pages, 3426 KiB

Open AccessArticle

Regionally Adaptive Active Learning Framework for Nuclear Segmentation in Microscopy Image

by Qian Wang, Jing Wei and Bo Quan

Electronics 2024, 13(17), 3430; https://doi.org/10.3390/electronics13173430 - 29 Aug 2024

Cited by 1 | Viewed by 852

Abstract

Recent innovations in tissue clearing and light-sheet microscopy allow the rapid acquisition of intact micron-resolution images in fluorescently labeled samples. Automated, accurate, and high-throughput nuclear segmentation methods are in high demand to quantify the number of cells and evaluate cell-type specific marker co-labeling. Complete quantification of cellular level differences in genetically manipulated animal models will allow localization of organ structural differences well beyond what has previously been accomplished through slice histology or MRI. This paper proposes a nuclei identification tool for accurate nuclear segmentation from tissue-cleared microscopy images by regionally adaptive active learning. We gradually improved high-level nuclei-to-nuclei contextual heuristics to determine a non-linear mapping from local image appearance to the segmentation label at the center of each local neighborhood. In addition, we propose an adaptive fine-tuning (FT) strategy to tackle the complex segmentation task of separating nuclei in close proximity, allowing for the precise quantification of structures where nuclei are often densely packed. Compared to the current nuclei segmentation methods, we have achieved more accurate and robust nuclear segmentation results in various complex scenarios. Full article

(This article belongs to the Special Issue Deep Learning Technology for Biomedical Signals and Images Applications)

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19 pages, 2826 KiB

Open AccessArticle

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

Cited by 2 | Viewed by 2138

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 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

Open AccessArticle

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 1069

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 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

Open AccessArticle

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

Cited by 2 | Viewed by 1377

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 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

Open AccessArticle

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 1641

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1266

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 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

Open AccessFeature PaperArticle

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 1437

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 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

Open AccessArticle

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 5 | Viewed by 1853

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 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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