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Applied Sciences
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Artificial Neural Network Applications in Healthcare and Biomedical Engineering
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Artificial Neural Network Applications in Healthcare and Biomedical Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 January 2025 | Viewed by 2866

Special Issue Editors

Dr. Hong Fu

E-Mail Website
Guest Editor
Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong, China
Interests: computer vision algorithms; artificial intelligence in healthcare and education; eye tracking and motion detection
Special Issues, Collections and Topics in MDPI journals
Dr. Tse-Tin Chan

E-Mail Website
Guest Editor
Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong, China
Interests: wireless communications; internet of things; machine learning in communications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, artificial neural networks (ANNs) have become popular for classification, clustering, pattern recognition, and prediction in many disciplines. Based on their practical performance and wide scope, ANNs work to solve the complex problems in the areas of Healthcare and Biomedical Engineering and to improve our life qualities.

This Special Issue entitled "Artificial Neural Network Applications in Healthcare and Biomedical Engineering" aims to collect more advances and applications of ANNs, including but not limited to the following topics:

  • Artificial neural network;
  • Pattern recognition;
  • Image processing;
  • Classification;
  • Machine learning;
  • Deep learning;
  • Biomedical Engineering;
  • Healthcare.

We welcome researchers to contribute to this Special Issue and share their findings regarding the wild future of artificial neural network applications.

Dr. Hong Fu
Dr. Tse-Tin Chan
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. Applied Sciences 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

  • artificial neural network
  • neural network
  • pattern recognition
  • machine learning
  • image processing

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Published Papers (3 papers)

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Research

23 pages, 58771 KiB  
Article
Enhancing Skin Lesion Classification Performance with the ABC Ensemble Model
by Jae-Young Choi, Min-Ji Song and You-Jin Shin
Appl. Sci. 2024, 14(22), 10294; https://doi.org/10.3390/app142210294 - 8 Nov 2024
Viewed by 542
Abstract
Skin cancer is one of the most easily developed cancers and is continuously seeing an increased incidence rate. In this study, we propose a novel ABC ensemble model for skin lesion classification by leveraging the ABCD rule, which is commonly used in dermatology [...] Read more.
Skin cancer is one of the most easily developed cancers and is continuously seeing an increased incidence rate. In this study, we propose a novel ABC ensemble model for skin lesion classification by leveraging the ABCD rule, which is commonly used in dermatology to evaluate lesion features such as asymmetry, border, color, and diameter. Our model consists of five distinct blocks, two of which focus on learning general image characteristics, while the remaining three focus on specialized features related to the ABCD rule. The final classification results are achieved through a weighted soft voting approach, where the generalization blocks are assigned higher weights to optimize performance. Through 15 experiments using various model configurations, we show that the weighted ABC ensemble model outperforms the baseline models, achieving the best performance with an accuracy of 0.9326 and an F1-score of 0.9302. Additionally, Grad-CAM analysis is employed to assess how each block in the ensemble focuses on distinct lesion features, further enhancing the interpretability and reliability of the model. Our findings demonstrate that integrating general image features with specific lesion characteristics improves classification performance, and that adjusting the soft voting weights yields optimal results. This novel model offers a reliable tool for early skin lesion diagnosis. Full article
(This article belongs to the Special Issue Artificial Neural Network Applications in Healthcare and Biomedical Engineering)
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19 pages, 20152 KiB  
Article
PAM-UNet: Enhanced Retinal Vessel Segmentation Using a Novel Plenary Attention Mechanism
by Yongmao Wang, Sirui Wu and Junhao Jia
Appl. Sci. 2024, 14(13), 5382; https://doi.org/10.3390/app14135382 - 21 Jun 2024
Viewed by 768
Abstract
Retinal vessel segmentation is critical for diagnosing related diseases in the medical field. However, the complex structure and variable size and shape of retinal vessels make segmentation challenging. To enhance feature extraction capabilities in existing algorithms, we propose PAM-UNet, a U-shaped network architecture [...] Read more.
Retinal vessel segmentation is critical for diagnosing related diseases in the medical field. However, the complex structure and variable size and shape of retinal vessels make segmentation challenging. To enhance feature extraction capabilities in existing algorithms, we propose PAM-UNet, a U-shaped network architecture incorporating a novel Plenary Attention Mechanism (PAM). In the BottleNeck stage of the network, PAM identifies key channels and embeds positional information, allowing spatial features within significant channels to receive more focus. We also propose a new regularization method, DropBlock_Diagonal, which discards diagonal regions of the feature map to prevent overfitting and enhance vessel feature learning. Within the decoder stage of the network, features from each stage are merged to enhance the segmentation accuracy of the final vessel. Experimental validation on two retinal image datasets, DRIVE and CHASE_DB1, shows that PAM-UNet achieves 97.15%, 83.16%, 98.45%, 83.15%, 98.66% and 97.64%, 85.82%, 98.46%, 82.56%, 98.95% on Acc, Se, Sp, F1, AUC, respectively, outperforming UNet and most other retinal vessel segmentation algorithms. Full article
(This article belongs to the Special Issue Artificial Neural Network Applications in Healthcare and Biomedical Engineering)
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16 pages, 3627 KiB  
Article
New Approach for Brain Tumor Segmentation Based on Gabor Convolution and Attention Mechanism
by Yuan Cao and Yinglei Song
Appl. Sci. 2024, 14(11), 4919; https://doi.org/10.3390/app14114919 - 6 Jun 2024
Viewed by 1033
Abstract
In the treatment process of brain tumors, it is of great importance to develop a set of MRI image segmentation methods with high accuracy and low cost. In order to extract the feature information for each region of the brain tumor more effectively, [...] Read more.
In the treatment process of brain tumors, it is of great importance to develop a set of MRI image segmentation methods with high accuracy and low cost. In order to extract the feature information for each region of the brain tumor more effectively, this paper proposes a new model Ga-U-Net based on Gabor convolution and an attention mechanism. Based on 3D U-Net, Gabor convolution is added at the shallow layer of the encoder, which is able to learn the local structure and texture information of the tumor better. After that, the CBAM attention mechanism is added after the output of each layer of the encoder, which not only enhances the network’s ability to perceive the brain tumor boundary information but also reduces some redundant information by allocating the attention to the two dimensions of space and channel. Experimental results show that the model performs well for multiple tumor regions (WT, TC, ET) on the brain tumor dataset BraTS 2021, with Dice coefficients of 0.910, 0.897, and 0.856, respectively, which are improved by 0.3%, 2%, and 1.7% compared to the base network, the U-Net network, with an average Dice of 0.887 and an average Hausdorff distance of 9.12, all of which are better than a few other state-of-the-art deep models for biomedical image segmentation. Full article
(This article belongs to the Special Issue Artificial Neural Network Applications in Healthcare and Biomedical Engineering)
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