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Symmetry
Special Issues
Symmetry and Asymmetry in Image Classification
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Symmetry and Asymmetry in Image Classification

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1158

Special Issue Editors

Dr. Ekkarat Boonchieng

E-Mail Website
Guest Editor
Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
Interests: computer graphics; image processing; data science; artificial intelligent; biomedical engineering
Prof. Dr. Suthep Suantai

E-Mail Website
Guest Editor
Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
Interests: image processing; data compression; image optimization; image analysis; image classification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are delighted to introduce a Special Issue of Symmetry, titled “Symmetry and Asymmetry in Image Classification”. Symmetry and asymmetry embody fundamental visual principles that profoundly influence how machines interpret and classify images. This collection invites contributions that examine how leveraging symmetrical structures—or intentionally incorporating asymmetrical patterns—can enhance classification robustness, accuracy, and efficiency. Topics of interest include symmetry-aware feature extraction, asymmetry-driven deep learning architectures, transformation-invariant classifiers, and applications spanning medical imaging, remote sensing, autonomous vision systems, and more. We welcome original research articles, comprehensive reviews, and innovative case studies that advance theoretical understanding or present novel practical applications. By illuminating how symmetry and asymmetry guide classification strategies, this Special Issue aims to provide a unique platform that bridges foundational research and cutting-edge solutions in image analysis.

We look forward to your contributions.

Dr. Ekkarat Boonchieng
Prof. Dr. Suthep Suantai
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 250 words) can be sent to the Editorial Office for assessment.

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. Symmetry 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 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

  • symmetry
  • image processing
  • data science
  • artificial intelligent
  • biomedical engineering
  • machine learning
  • symmetric algorithms
  • visual data analysis
  • symmetric patterns
  • patterns analysis

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Published Papers (1 paper)

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Research

32 pages, 9891 KB  
Article
Attention-Based Deep Learning Framework for Lung Nodule Classification in CT Images
by Vinayak K. Bairagi, Aparna Rajesh Lokhande, Shweta Sadanand Salunkhe, Ekkarat Boonchieng and Preeti Topannavar
Symmetry 2026, 18(3), 431; https://doi.org/10.3390/sym18030431 - 28 Feb 2026
Viewed by 633
Abstract
Lung cancer continues to be one of the leading causes of cancer-related deaths worldwide, as pulmonary nodules are often diagnosed at later stages. Therefore, accurate nodule classification is crucial for enabling early detection and supporting timely clinical decision-making. This study proposes a computer-aided [...] Read more.
Lung cancer continues to be one of the leading causes of cancer-related deaths worldwide, as pulmonary nodules are often diagnosed at later stages. Therefore, accurate nodule classification is crucial for enabling early detection and supporting timely clinical decision-making. This study proposes a computer-aided diagnosis (CAD) system for lung nodule classification using computed tomography (CT) images, specifically focused on malignancy prediction and structural morphology analysis. The proposed framework is based on a novel attention-based Convolutional Neural Network (CNN) that incorporates both channel-wise and spatial attention mechanisms. This dual-attention structure enables the model to emphasize diagnostically relevant features while suppressing irrelevant background information, thereby improving interpretability and classification accuracy. For benchmarking purposes, CNN, CNN-SVM, and ResNet101 architectures were implemented for comparison. Experimental results on the LIDC-IDRI dataset for binary classification (benign vs. malignant) and on the IQ-OTH/NCCD dataset for both binary and three-class (normal, benign, malignant) classification tasks demonstrate that the proposed Attention-Based CNN outperforms all baseline models, achieving a maximum classification accuracy of 98% in the binary setting. In addition to accuracy, the proposed model achieves strong performance across multiple evaluation metrics, including precision, recall, F1-score, AUC, and separately reported confusion matrices for both binary and multiclass evaluations, indicating the robustness of the approach. The dual-attention mechanism enhances salient feature localization and discriminative representation learning, thereby contributing to improved performance in both binary and multiclass classification tasks Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Image Classification)
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