Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.6
Journals
Electronics
Special Issues
New Machine Learning Technologies for Biomedical Applications
share announcement

New Machine Learning Technologies for Biomedical Applications

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

Deadline for manuscript submissions: closed (25 May 2023) | Viewed by 12438

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. Morad Danishvar

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Systems Engineering Research Group, Brunel University, London, UK
Interests: data analytics, machine learning and AI; SCADA; digital manufacturing; industry 4.0; decision support systems; simulation and applications
Dr. Seyed Naser Razavi

E-Mail Website
Guest Editor
ADAPT Centre, Trinity College Dublin, D02 PN40 Dublin, Ireland
Interests: artificial intelligence; machine learning; machine vision; NLP; deep learning

Special Issue Information

Dear Colleagues, 

We are pleased to invite you to publish the state-of-the-art research findings of new Machine Learning Technologies for Biomedical Applications. The use of machine learning and artificial intelligence (AI) technologies in medicine and healthcare has been on the rise and these techniques play an essential role in the development of wise, affordable, and cost-effective healthcare systems.

The machine learning field is still somewhat new to many medical researchers and is a considerable obstacle for many medical researchers to understand and adapt to this new tool due to its inexplicability and interpretability. 

This Special Issue aims to discuss practical means for developing and assessing the viability of machine learning technologies and facilitate the knowledge and adaptation of this emerging field to a more general research community.  

Toward this end, we invite submissions and publications of articles that build bridges between machine learning research and its clinical applications.

Dr. Sebelan Danishvar
Dr. Morad Danishvar
Dr. Seyed Naser Razavi
Guest Editors

Technical Committee Member:
Mr. Abd Al Rahman M. Abu Ebayyeh from Brunel University

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

  • machine learning
  • artificial intelligence
  • biomedical applications
  • physiological signals
  • image analysis

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 4355 KiB  
Article
A Customized ECA-CRNN Model for Emotion Recognition Based on EEG Signals
by Yan Song, Yiming Yin and Panfeng Xu
Electronics 2023, 12(13), 2900; https://doi.org/10.3390/electronics12132900 - 1 Jul 2023
Cited by 4 | Viewed by 1474
Abstract
Electroencephalogram (EEG) signals are electrical signals generated by changes in brain potential. As a significant physiological signal, EEG signals have been applied in various fields, including emotion recognition. However, current deep learning methods based on EEG signals for emotion recognition lack consideration of [...] Read more.
Electroencephalogram (EEG) signals are electrical signals generated by changes in brain potential. As a significant physiological signal, EEG signals have been applied in various fields, including emotion recognition. However, current deep learning methods based on EEG signals for emotion recognition lack consideration of important aspects and comprehensive analysis of feature extraction interactions. In this paper, we propose a novel model named ECA-CRNN for emotion recognition using EEG signals. Our model integrates the efficient channel attention (ECA-Net) module into our modified combination of a customized convolutional neural network (CNN) and gated circulation unit (GRU), which enables more comprehensive feature extraction, enhances the internal relationship between frequency bands and improves recognition performance. Additionally, we utilize four-dimensional data as input to our model, comprising temporal, spatial and frequency information. The test on the DEAP dataset demonstrates that it enhances the recognition accuracy of EEG signals in both arousal and valence to 95.70% and 95.33%, respectively, while also reducing the standard deviation during five-fold cross-validation to 1.16 and 1.45 for arousal and valence, respectively, surpassing most methods. Full article
(This article belongs to the Special Issue New Machine Learning Technologies for Biomedical Applications)
►▼ Show Figures

Figure 1

14 pages, 3157 KiB  
Article
Improvement of Retinal Vessel Segmentation Method Based on U-Net
by Ning Wang, Kefeng Li, Guangyuan Zhang, Zhenfang Zhu and Peng Wang
Electronics 2023, 12(2), 262; https://doi.org/10.3390/electronics12020262 - 4 Jan 2023
Cited by 4 | Viewed by 1918
Abstract
Retinal vessel segmentation remains a challenging task because the morphology of the retinal vessels reflects the health of a person, which is essential for clinical diagnosis. Therefore, achieving accurate segmentation of the retinal vessel shape can determine the patient’s physical condition in a [...] Read more.
Retinal vessel segmentation remains a challenging task because the morphology of the retinal vessels reflects the health of a person, which is essential for clinical diagnosis. Therefore, achieving accurate segmentation of the retinal vessel shape can determine the patient’s physical condition in a timely manner and can prevent blindness in patients. Since the traditional retinal vascular segmentation method is manually operated, this can be time-consuming and laborious. With the development of convolutional neural networks, U-shaped networks (U-Nets) and variants show good performance in image segmentation. However, U-Net is prone to feature loss due to the operation of the encoder convolution layer and also causes the problem of mismatch in the processing of contextual information features caused by the skip connection part. Therefore, we propose an improvement of the retinal vessel segmentation method based on U-Net to segment retinal vessels accurately. In order to extract more features from encoder features, we replace the convolutional layer with ResNest network structure in feature extraction, which aims to enhance image feature extraction. In addition, a Depthwise FCA Block (DFB) module is proposed to deal with the mismatched processing of local contextual features by skip connections. Combined with the two public datasets on retinal vessel segmentation, namely DRIVE and CHASE_DB1, and comparing our method with a larger number of networks, the experimental results confirmed the effectiveness of the proposed method. Our method is better than most segmentation networks, demonstrating the method’s significant clinical value. Full article
(This article belongs to the Special Issue New Machine Learning Technologies for Biomedical Applications)
►▼ Show Figures

Figure 1

24 pages, 3940 KiB  
Article
Proposed Model for Secured Data Storage in Decentralized Cloud by Blockchain Ethereum
by Nabeel Khan, Hanan Aljoaey, Mujahid Tabassum, Ali Farzamnia, Tripti Sharma and Yew Hoe Tung
Electronics 2022, 11(22), 3686; https://doi.org/10.3390/electronics11223686 - 10 Nov 2022
Cited by 13 | Viewed by 3163
Abstract
Since cloud computing is an essential component of any modern company (usually accounting for a considerable share of information technology (IT) infrastructure investment), consumers rely on cloud services. Data privacy and security are worries when data remains in third-party storage. Existing document version [...] Read more.
Since cloud computing is an essential component of any modern company (usually accounting for a considerable share of information technology (IT) infrastructure investment), consumers rely on cloud services. Data privacy and security are worries when data remains in third-party storage. Existing document version control systems are centralized and at risk from data loss, as seen by higher time utilization and incorrect document update procedures that allow modifications to a document without the awareness of other network operators. Underutilized peer resources might be leveraged to construct storage. According to this argument, an elevated level of data security may be obtained by encrypting the data and dispersing it among numerous nodes. In this study, we attempted to review the security of cloud systems when using the blockchain Ethereum, and cloud computing was briefly discussed with its advantages and disadvantages. The idea of a decentralized cloud was briefly demonstrated with blockchain technology. Furthermore, previous papers were reviewed and presented in tabular form. This dictated that there are still research gaps in the field of blockchain-based cloud computing systems. This study proposed a model for secured data storage over a decentralized cloud by blockchain Ethereum. Full article
(This article belongs to the Special Issue New Machine Learning Technologies for Biomedical Applications)
►▼ Show Figures

Figure 1

21 pages, 1188 KiB  
Article
Drug Adverse Event Detection Using Text-Based Convolutional Neural Networks (TextCNN) Technique
by Ashish Rawat, Mudasir Ahmad Wani, Mohammed ElAffendi, Ali Shariq Imran, Zenun Kastrati and Sher Muhammad Daudpota
Electronics 2022, 11(20), 3336; https://doi.org/10.3390/electronics11203336 - 17 Oct 2022
Cited by 5 | Viewed by 2593
Abstract
With the rapid advancement in healthcare, there has been exponential growth in the healthcare records stored in large databases to help researchers, clinicians, and medical practitioner’s for optimal patient care, research, and trials. Since these studies and records are lengthy and time consuming [...] Read more.
With the rapid advancement in healthcare, there has been exponential growth in the healthcare records stored in large databases to help researchers, clinicians, and medical practitioner’s for optimal patient care, research, and trials. Since these studies and records are lengthy and time consuming for clinicians and medical practitioners, there is a demand for new, fast, and intelligent medical information retrieval methods. The present study is a part of the project which aims to design an intelligent medical information retrieval and summarization system. The whole system comprises three main modules, namely adverse drug event classification (ADEC), medical named entity recognition (MNER), and multi-model text summarization (MMTS). In the current study, we are presenting the design of the ADEC module for classification tasks, where basic machine learning (ML) and deep learning (DL) techniques, such as logistic regression (LR), decision tree (DT), and text-based convolutional neural network (TextCNN) are employed. In order to perform the extraction of features from the text data, TF-IDF and Word2Vec models are employed. To achieve the best performance of the overall system for efficient information retrieval and summarization, an ensemble strategy is employed, where predictions of the selected base models are integrated to boost the robustness of one model. The performance results of all the models are recorded as promising. TextCNN, with an accuracy of 89%, performs better than the conventional machine learning approaches, i.e., LR and DT with accuracies of 85% and 77%, respectively. Furthermore, the proposed TextCNN outperforms the existing adverse drug event classification approaches, achieving precision, recall, and an F1 score of 87%, 91%, and 89%, respectively. Full article
(This article belongs to the Special Issue New Machine Learning Technologies for Biomedical Applications)
►▼ Show Figures

Figure 1

20 pages, 6067 KiB  
Article
FirecovNet: A Novel, Lightweight, and Fast Deep Learning-Based Network for Detecting COVID-19 Patients Using Chest X-rays
by Leila Hassanlou, Saeed Meshgini, Reza Afrouzian, Ali Farzamnia and Ervin Gubin Moung
Electronics 2022, 11(19), 3068; https://doi.org/10.3390/electronics11193068 - 26 Sep 2022
Viewed by 1866
Abstract
At the end of 2019, a new virus (SARS-CoV-2) called COVID-19 was reported in Wuhan, China, and spread rapidly worldwide. After two years later, several variants of this virus were created, infecting 608 million people and causing 6.51 million deaths. Due to the [...] Read more.
At the end of 2019, a new virus (SARS-CoV-2) called COVID-19 was reported in Wuhan, China, and spread rapidly worldwide. After two years later, several variants of this virus were created, infecting 608 million people and causing 6.51 million deaths. Due to the insufficient sensitivity of RT-PCR test kits, one of the main tools for detecting the virus, chest X-ray images are a popular tool for diagnosing the virus in patients with respiratory symptoms. Models based on deep learning are showing promising results in combating this pandemic. A novel convolutional neural network, FirecovNet, is suggested in this study that detects COVID-19 infection automatically based on raw chest X-ray images. With an architecture inspired by the integration of DarkNet and SqueezeNet networks, the proposed model has fewer parameters than state-of-the-art models and is trained using COVID-19, bacterial pneumonia, normal, lung opacity, and viral pneumonia images, which were collected from two public datasets and also are symmetric in the distribution in class. FirecovNet performance has been verified using the stratified 5-fold cross-validation method. A total of five classification tasks are performed, including four 4-class classifications, and one 5-class classification, and the accuracy of all tasks was at least 95.9%. For all classification tasks, the proposed network has demonstrated promising results in precision, sensitivity, and F1-score. Moreover, a comparison was made between the proposed network and eight deep transfer learning networks and in terms of accuracy, precision, sensitivity, F1-score, speed, and size of the saved model; FirecovNet was very promising. Therefore, FirecovNet can be useful as a tool for more accurate diagnosis of the COVID-19 virus, along with diagnostic tests, in situations where the number of specialist radiologists may be limited. Full article
(This article belongs to the Special Issue New Machine Learning Technologies for Biomedical Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop