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Diagnostics
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Impact of Deep Learning in Biomedical Engineering
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Impact of Deep Learning in Biomedical Engineering

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 3795

Special Issue Editors

Dr. Karthik Ramamurthy

E-Mail Website
Guest Editor
Centre for Cyber Physical Systems, School of Electronics Engineering, Vellore Institute of Technology, Chennai 600127, India
Interests: biomedical signal and image processing; deep learning; medical image processing; machine learning; artificial intelligence; healthcare
Dr. Menaka Radhakrishnan

E-Mail Website
Guest Editor
Centre for Cyber Physical Systems, School of Electronics Engineering, Vellore Institute of Technology, Chennai 600127, India
Interests: biomedical signal and image processing; deep learning; medical image processing; machine learning; artificial intelligence; healthcare
Dr. Daehan Won

E-Mail Website
Guest Editor
Systems Science and Industrial Engineering, Binghamton University State University of New York, New York, NY 13902-6000, USA
Interests: large scaled data analysis via mathematical programming and algorithms

Special Issue Information

Dear Colleagues,

Understanding and using complex, high-dimensional, and heterogeneous biological data continues to be a major challenge in the transformation of healthcare. Feature engineering is generally required in traditional data mining and statistical learning techniques for building prediction models to extract useful and more robust features from data. New efficient paradigms for creating end-to-end learning models from complex data are provided by the most recent advancements in deep learning.

The aim of this Special Issue is to examine the state-of-the-art deep learning techniques employed for different problems in the field of biomedical engineering. We invite authors to contribute original research articles and reviews related to deep learning for biomedical engineering. Articles that examine cutting-edge deep learning methods will be highly appreciated. 

Dr. Karthik Ramamurthy
Dr. Menaka Radhakrishnan
Dr. Daehan Won
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. Diagnostics 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.

 

Keywords

  • deep learning
  • biomedical engineering
  • convolutional neural networks
  • recurrent neural networks
  • reinforcement learning
  • neuroimaging
  • diagnostic imaging
  • medical imaging
  • biosignal processing

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Published Papers (3 papers)

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Research

25 pages, 5742 KiB  
Article
A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification
by Yezi Ali Kadhim, Mehmet Serdar Guzel and Alok Mishra
Diagnostics 2024, 14(14), 1469; https://doi.org/10.3390/diagnostics14141469 - 9 Jul 2024
Viewed by 759
Abstract
Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In [...] Read more.
Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep learning techniques, which were based on a convolutional neural network (CNN) or autoencoder, to extract features and combine them with the next step of the meta-heuristic algorithm in order to select optimal features using the particle swarm optimization (PSO) algorithm. This combination sought to reduce the dimensionality of the datasets while maintaining the original performance of the data. This is considered an innovative method and ensures highly accurate classification results across various medical datasets. Several classifiers were employed to predict the diseases. The COVID-19 dataset found that the highest accuracy was 99.76% using the combination of CNN-PSO-SVM. In comparison, the brain tumor dataset obtained 99.51% accuracy, the highest accuracy derived using the combination method of autoencoder-PSO-KNN. Full article
(This article belongs to the Special Issue Impact of Deep Learning in Biomedical Engineering)
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18 pages, 3282 KiB  
Article
Integration of Localized, Contextual, and Hierarchical Features in Deep Learning for Improved Skin Lesion Classification
by Karthik Ramamurthy, Illakiya Thayumanaswamy, Menaka Radhakrishnan, Daehan Won and Sindhia Lingaswamy
Diagnostics 2024, 14(13), 1338; https://doi.org/10.3390/diagnostics14131338 - 24 Jun 2024
Viewed by 758
Abstract
Skin lesion classification is vital for the early detection and diagnosis of skin diseases, facilitating timely intervention and treatment. However, existing classification methods face challenges in managing complex information and long-range dependencies in dermoscopic images. Therefore, this research aims to enhance the feature [...] Read more.
Skin lesion classification is vital for the early detection and diagnosis of skin diseases, facilitating timely intervention and treatment. However, existing classification methods face challenges in managing complex information and long-range dependencies in dermoscopic images. Therefore, this research aims to enhance the feature representation by incorporating local, global, and hierarchical features to improve the performance of skin lesion classification. We introduce a novel dual-track deep learning (DL) model in this research for skin lesion classification. The first track utilizes a modified Densenet-169 architecture that incorporates a Coordinate Attention Module (CoAM). The second track employs a customized convolutional neural network (CNN) comprising a Feature Pyramid Network (FPN) and Global Context Network (GCN) to capture multiscale features and global contextual information. The local features from the first track and the global features from second track are used for precise localization and modeling of the long-range dependencies. By leveraging these architectural advancements within the DenseNet framework, the proposed neural network achieved better performance compared to previous approaches. The network was trained and validated using the HAM10000 dataset, achieving a classification accuracy of 93.2%. Full article
(This article belongs to the Special Issue Impact of Deep Learning in Biomedical Engineering)
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15 pages, 3011 KiB  
Article
STSN-Net: Simultaneous Tooth Segmentation and Numbering Method in Crowded Environments with Deep Learning
by Shaofeng Wang, Shuang Liang, Qiao Chang, Li Zhang, Beiwen Gong, Yuxing Bai, Feifei Zuo, Yajie Wang, Xianju Xie and Yu Gu
Diagnostics 2024, 14(5), 497; https://doi.org/10.3390/diagnostics14050497 - 26 Feb 2024
Cited by 4 | Viewed by 1438
Abstract
Accurate tooth segmentation and numbering are the cornerstones of efficient automatic dental diagnosis and treatment. In this paper, a multitask learning architecture has been proposed for accurate tooth segmentation and numbering in panoramic X-ray images. A graph convolution network was applied for the [...] Read more.
Accurate tooth segmentation and numbering are the cornerstones of efficient automatic dental diagnosis and treatment. In this paper, a multitask learning architecture has been proposed for accurate tooth segmentation and numbering in panoramic X-ray images. A graph convolution network was applied for the automatic annotation of the target region, a modified convolutional neural network-based detection subnetwork (DSN) was used for tooth recognition and boundary regression, and an effective region segmentation subnetwork (RSSN) was used for region segmentation. The features extracted using RSSN and DSN were fused to optimize the quality of boundary regression, which provided impressive results for multiple evaluation metrics. Specifically, the proposed framework achieved a top F1 score of 0.9849, a top Dice metric score of 0.9629, and an mAP (IOU = 0.5) score of 0.9810. This framework holds great promise for enhancing the clinical efficiency of dentists in tooth segmentation and numbering tasks. Full article
(This article belongs to the Special Issue Impact of Deep Learning in Biomedical Engineering)
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