Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.9
2.4
Journals
Information
Special Issues
Disease Diagnosis Based on Medical Images and Signals
share announcement

Disease Diagnosis Based on Medical Images and Signals

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Biomedical Information and Health".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 527

Special Issue Editors

Dr. Se-woon Choe

E-Mail Website
Guest Editor
Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea
Interests: biomedical optics; medical image analysis; biosignal processing; artificial intelligence; cancer diagnosis/therapy
Dr. Gelan Ayana

E-Mail Website
Guest Editor
Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea
Interests: medical image analysis; biosignal processing; deep learning; transfer learning

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue titled “Disease Diagnosis Based on Medical Images and Signals”, featuring high-quality articles and reviews on advanced computational methodologies in disease diagnosis, focusing on medical image analysis, medical signal processing, deep learning, and transfer learning. Medical image analysis includes innovative techniques for interpreting complex data from imaging modalities such as MRI, CT, microscopy, endoscopy, mammography, and ultrasound. These techniques are crucial for diagnosing various conditions, as they enhance diagnostic accuracy and efficiency through automated disease detection. Medical signal processing involves analyzing physiological signals like ECG, EEG, and EMG. Advances in this field support precise monitoring and the early detection of heart, brain, and muscle irregularities, with machine learning facilitating real-time analysis and remote monitoring. Deep learning, especially using convolutional neural networks (CNNs) and vision transformers (ViTs), has revolutionized medical image analysis and signal processing. These models identify intricate patterns and correlations, improving accuracy and reliability. Transfer learning enhances these capabilities by adapting models from large, generic datasets to specific medical contexts, addressing limited annotated data and accelerating diagnostic tool development. This Special Issue will highlight groundbreaking research integrating these areas, thus advancing diagnostic precision, efficiency, and accessibility in healthcare.

Dr. Se-woon Choe
Dr. Gelan Ayana
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. Information is an international peer-reviewed open access monthly 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 1600 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

  • medical image analysis
  • medical signal processing
  • deep learning
  • transfer learning

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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

Research

15 pages, 2011 KiB  
Article
A Lightweight Neural Network for Cell Segmentation Based on Attention Enhancement
by Shuang Xia, Qian Sun, Yiheng Zhou, Zhaoyuxuan Wang, Chaoxing You, Kainan Ma and Ming Liu
Information 2025, 16(4), 295; https://doi.org/10.3390/info16040295 - 8 Apr 2025
Viewed by 374
Abstract
Deep neural networks have made significant strides in medical image segmentation tasks, but their large-scale parameters and high computational complexity limit their applicability on resource-constrained edge devices. To address this challenge, this paper introduces a lightweight nuclear segmentation network called Attention-Enhanced U-Net (AttE-Unet) [...] Read more.
Deep neural networks have made significant strides in medical image segmentation tasks, but their large-scale parameters and high computational complexity limit their applicability on resource-constrained edge devices. To address this challenge, this paper introduces a lightweight nuclear segmentation network called Attention-Enhanced U-Net (AttE-Unet) for cell segmentation. AttE-Unet enhances the network’s feature extraction capabilities through an attention mechanism and combines the strengths of deep learning with traditional image filtering algorithms, while substantially reducing computational and storage demands. Experimental results on the PanNuke dataset demonstrate that AttE-Unet, despite its significant reduction in model size—with the number of parameters and floating-point operations per second reduced to 1.57% and 0.1% of the original model, respectively—still maintains a high level of segmentation performance. Specifically, the F1 score and Intersection over Union (IoU) score are 91.7% and 89.3% of the original model’s scores. Furthermore, deployment on an MCU consumes only 2.09 MB of Flash and 1.38 MB of RAM, highlighting the model’s lightweight nature and its potential for practical deployment as a medical image segmentation solution on edge devices. Full article
(This article belongs to the Special Issue Disease Diagnosis Based on Medical Images and Signals)
►▼ Show Figures

Figure 1

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