Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
3.0
Journals
Diagnostics
Special Issues
Classification of Diseases Using Machine Learning Algorithms: 2nd Edition
share announcement

Classification of Diseases Using Machine Learning Algorithms: 2nd Edition

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: 30 September 2025 | Viewed by 306

Special Issue Editor

Dr. Derya Avci

E-Mail Website
Guest Editor
Department of Software Engineering, Technology Faculty, Firat University, 23119 Elazig, Turkey
Interests: pattern recognition; image processing; intelligent systems; artificial intelligence systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medical diagnosis is the basis of effective central distribution and guidance. In this process, multiple methods and tools are used together to accurately and systematically evaluate patients.

The classification of diseases using machine learning algorithms is an active area of research in the field of medical informatics. With the increasing amount of medical data generated, machine learning algorithms have the potential to assist physicians and researchers in identifying patterns and making more accurate diagnoses.

In this Special Issue, we aim to publish a collection of studies on machine learning algorithms in the classification of diseases using medical signals, medical data, and medical images. The medical images of various organs from different X-ray modalities, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), and analysis of patient data (e.g., blood tests and vital signs) can suggest possible diagnoses, etc., and can be employed for disease detection. We hope to publish many original and deep-learning papers applied to medicine to improve the quality of decision making.

The Special Issue focuses on the “Classification of Diseases Using Machine Learning Algorithms”. We welcome the submission of high-quality original and foundational research, reviews, and case reports.

Dr. Derya Avci
Guest Editor

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

  • artificial intelligence
  • classification of diseases
  • machine learning
  • medical image processing
  • deep learning
  • neural network
  • decision support systems

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

19 pages, 3770 KiB  
Article
A New Pes Planus Automatic Diagnosis Method: ViT-OELM Hybrid Modeling
by Derya Avcı
Diagnostics 2025, 15(7), 867; https://doi.org/10.3390/diagnostics15070867 - 28 Mar 2025
Viewed by 214
Abstract
Background/Objectives: Pes planus (flat feet) is a condition characterized by flatter than normal soles of the foot. In this study, a Vision Transformer (ViT)-based deep learning architecture is proposed to automate the diagnosis of pes planus. The model analyzes foot images and classifies [...] Read more.
Background/Objectives: Pes planus (flat feet) is a condition characterized by flatter than normal soles of the foot. In this study, a Vision Transformer (ViT)-based deep learning architecture is proposed to automate the diagnosis of pes planus. The model analyzes foot images and classifies them into two classes, as “pes planus” and “not pes planus”. In the literature, models based on Convolutional neural networks (CNNs) can automatically perform such classification, regression, and prediction processes, but these models cannot capture long-term addictions and general conditions. Methods: In this study, the pes planus dataset, which is openly available on the Kaggle database, was used. This paper suggests a ViT-OELM hybrid model for automatic diagnosis from the obtained pes planus images. The suggested ViT-OELM hybrid model includes an attention mechanism for feature extraction from the pes planus images. A total of 1000 features obtained for each sample image from this attention mechanism are used as inputs for an Optimum Extreme Learning Machine (OELM) classifier using various activation functions, and are classified. Results: In this study, the performance of this suggested ViT-OELM hybrid model is compared with some other studies, which used the same pes planus database. These comparison results are given. The suggested ViT-OELM hybrid model was trained for binary classification. The performance metrics were computed in testing phase. The model showed 98.04% accuracy, 98.04% recall, 98.05% precision, and an F-1 score of 98.03%. Conclusions: Our suggested ViT-OELM hybrid model demonstrates superior performance compared to those of other studies, which used the same dataset, in the literature. Full article
(This article belongs to the Special Issue Classification of Diseases Using Machine Learning Algorithms: 2nd Edition)
►▼ 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