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Diagnostics
Special Issues
Recent Advances in Epidemiological Diagnostics: Detecting and Controlling Infectious Diseases
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Recent Advances in Epidemiological Diagnostics: Detecting and Controlling Infectious Diseases

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Diagnostic Microbiology and Infectious Disease".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 572

Special Issue Editors

Dr. Sri Banerjee

E-Mail Website
Guest Editor
School of Health Sciences and Public Policy Core Faculty, Walden University, 100 Washington Avenue South Suite 1210, Minneapolis, MN 55401, USA
Interests: public health; global health; infection diseases; cardiovascular health; chronic disease; mental health; global health; social epidemiology; cancer; injury prevention
Dr. W. Sumner Davis

E-Mail
Guest Editor Assistant
College of Health Sciences, Walden University, Minneapolis, MN 55401, USA
Interests: public health; global health; infection diseases

Special Issue Information

Dear Colleagues,

This Special Issue, "Recent Advances in Epidemiological Diagnostics: Detecting and Controlling Infectious Diseases", explores the cutting-edge methodologies and innovations in the field of epidemiological diagnostics. It compiles a diverse range of manuscripts encompassing novel diagnostic techniques, rapid test developments, and advanced data analytics for the early detection of infectious diseases. Contributions also delve into strategies for effective disease control, highlighting the integration of technology with public health measures. By showcasing these advancements, the Special Issue aims to foster a deeper understanding of how scientific progress can help mitigate the impact of infectious diseases globally.

Dr. Sri Banerjee
Guest Editor

Dr. W. Sumner Davis
Guest Editor Assistant

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

  • epidemiological diagnostics
  • disease control
  • clinical practice and patient care
  • public health
  • infectious diseases

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

Published Papers (1 paper)

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Research

17 pages, 3477 KiB  
Article
Breaking Diagnostic Barriers: Vision Transformers Redefine Monkeypox Detection
by Gelan Ayana, Beshatu Debela Wako, So-yun Park, Jude Kong, Sahng Min Han, Soon-Do Yoon and Se-woon Choe
Diagnostics 2025, 15(13), 1698; https://doi.org/10.3390/diagnostics15131698 - 3 Jul 2025
Viewed by 365
Abstract
Background/Objective: The global spread of Monkeypox (Mpox) has highlighted the urgent need for rapid, accurate diagnostic tools. Traditional methods like polymerase chain reaction (PCR) are resource-intensive, while skin image-based detection offers a promising alternative. This study evaluates the effectiveness of vision transformers (ViTs) [...] Read more.
Background/Objective: The global spread of Monkeypox (Mpox) has highlighted the urgent need for rapid, accurate diagnostic tools. Traditional methods like polymerase chain reaction (PCR) are resource-intensive, while skin image-based detection offers a promising alternative. This study evaluates the effectiveness of vision transformers (ViTs) for automated Mpox detection. Methods: By fine-tuning a pre-trained ViT model on an Mpox lesion image dataset, a robust ViT-based transfer learning (TL) model was created. Performance was assessed relative to convolutional neural network (CNN)-based TL models and ViT models trained from scratch across key metrics: accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Furthermore, a transferability measure was utilized to assess the effectiveness of feature transfer to Mpox images. Results: The results show that the ViT model outperformed a CNN, achieving an AUC of 0.948 and an accuracy of 0.942 with a p-value of less than 0.05 across all metrics, highlighting its potential for accurate and scalable Mpox detection. Moreover, the ViT models yielded a better hypothesis margin-based transferability measure, highlighting its effectiveness in transferring useful learning weights to Mpox images. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations also confirmed that the ViT model attends to clinically relevant features, supporting its interpretability and reliability for diagnostic use. Conclusions: The results from this study suggest that ViT offers superior accuracy, making it a valuable tool for Mpox early detection in field settings, especially where conventional diagnostics are limited. This approach could support faster outbreak response and improved resource allocation in public health systems. Full article
(This article belongs to the Special Issue Recent Advances in Epidemiological Diagnostics: Detecting and Controlling Infectious Diseases)
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