Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.2
Journals
Symmetry
Special Issues
Asymmetric and Symmetric in Deep Computer Vision and Generative Modeling
share announcement

Asymmetric and Symmetric in Deep Computer Vision and Generative Modeling

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

Deadline for manuscript submissions: 31 March 2026 | Viewed by 7383

Special Issue Editors

Prof. Dr. Youssef Es-Saady

E-Mail
Guest Editor
Polydisciplinary Faculty of Taroudant, Ibnou Zohr University, Taroudant 83000, Morocco
Interests: image processing; computer vision; machine learning; edge computing; natural language processing
Prof. Dr. Mohamed El Hajji

E-Mail
Guest Editor
Centre Régional des Métiers de l'Education et de la Formation Souss Massa, Agadir 106, Morocco
Interests: artificial intelligence; computer vision; systems engineering; e-learning; instructional design

Special Issue Information

Dear Colleagues,

In recent years, deep learning has transformed the fields of computer vision and generative modeling, enabling remarkable advancements across a wide range of applications, from image recognition and natural scene interpretation to the generation of realistic synthetic data. Symmetry and asymmetry are central to these fields, influencing the efficiency of model architectures, optimization techniques, and adaptability to varied data conditions. Symmetric techniques often support generalization and model stability, while asymmetric approaches provide flexible solutions to complex challenges, such as data variability and the handling of noise and anomalies.

This Special Issue seeks to showcase pioneering research that delves into the applications, theoretical insights, and methodological advancements of symmetric and asymmetric techniques within deep computer vision and generative modeling. We invite contributions that expand upon these frameworks to enhance model generalization, efficiency, and interpretability, spanning fundamental theory to practical applications. This collection of articles aims to provide a comprehensive understanding of the impact of these approaches, fostering further development in these rapidly evolving fields. Topics of interest include, but are not limited to, the following:

  • Symmetric and asymmetric neural architectures in computer vision and generative tasks;
  • Deep learning methods for pattern recognition, segmentation, and image generation;
  • Generative models for image synthesis, inpainting, and super-resolution;
  • Symmetry in feature extraction, data augmentation, and transfer learning;
  • Asymmetric model training, loss functions, and optimization for vision applications;
  • Cross-domain generative modeling and data augmentation;
  • Use of symmetry in reducing model bias and enhancing interpretability;
  • Challenges and solutions in asymmetric and symmetric data fusion;
  • Generative adversarial networks (GANs), autoencoders, and transformers for image synthesis;
  • Multimodal learning analytics;
  • Symmetry in handling data variability and augmenting model robustness;
  • Applications in fields like medical imaging, autonomous driving, and environmental monitoring;

We encourage researchers to contribute their original research articles, comprehensive reviews, or technical perspectives that explore the pivotal role of symmetry and asymmetry, driving forward the capabilities of deep computer vision and generative modeling. This Special Issue seeks to inspire new ideas, foster collaboration, and contribute to shaping the future of these dynamic research areas.

Prof. Dr. Youssef Es-Saady
Prof. Dr. Mohamed El Hajji
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. 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

  • deep computer vision
  • generative models and GANs
  • symmetry and asymmetry in neural networks
  • feature extraction and representation learning
  • synthetic data generation
  • cross-domain applications
  • data augmentation and transfer learning
  • model robustness and interpretability

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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (5 papers)

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

Research

Jump to: Review

24 pages, 5085 KiB  
Article
Stellar-YOLO: A Graphite Ore Grade Detection Method Based on Improved YOLO11
by Zeyang Qiu, Xueyu Huang, Sifan Li and Jionghui Wang
Symmetry 2025, 17(6), 966; https://doi.org/10.3390/sym17060966 - 18 Jun 2025
Viewed by 898
Abstract
Mineral recognition technology is crucial for improving mining efficiency and advancing smart mining development. To enable the efficient deployment of graphite ore grade detection on edge computing devices, we propose Stellar-YOLO, a YOLO11-based detection framework with asymmetrical architecture optimizations tailored for real-world conditions. [...] Read more.
Mineral recognition technology is crucial for improving mining efficiency and advancing smart mining development. To enable the efficient deployment of graphite ore grade detection on edge computing devices, we propose Stellar-YOLO, a YOLO11-based detection framework with asymmetrical architecture optimizations tailored for real-world conditions. The backbone is replaced by the lightweight StarNet to enhance computational efficiency, while the C3k2-CAS module, integrating convolution and additive attention, is embedded in the neck to improve feature expressiveness. The head incorporates the SEAM module, forming the Detect-SEAM, to boost the recognition of complex mineral details. Moreover, to robustly adapt to real mining environments, we apply simulated data augmentation techniques involving motion blur, dust noise, and low brightness conditions. Stellar-YOLO achieves 93.6% mAP based on a custom-built graphite ore dataset, outperforming the baseline by 4.5% and reducing the FLOPs, parameters, and model size by 27%, 26%, and 23%, respectively. This work explores how asymmetrical architectural innovations and robustness-oriented evaluation contribute to a lightweight and effective approach for computer vision-based mineral quality assessment, demonstrating strong potential for practical applications in real-world industrial environments. Full article
(This article belongs to the Special Issue Asymmetric and Symmetric in Deep Computer Vision and Generative Modeling)
►▼ Show Figures

Figure 1

33 pages, 4127 KiB  
Article
Kinematic Skeleton Extraction from 3D Model Based on Hierarchical Segmentation
by Nitinan Mata and Sakchai Tangwannawit
Symmetry 2025, 17(6), 879; https://doi.org/10.3390/sym17060879 - 4 Jun 2025
Viewed by 576
Abstract
A new approach for skeleton extraction has been designed to work directly with 3D point cloud data. It blends hierarchical segmentation with a multi-scale ensemble built on top of modified PointNet models. Outputs from three network variants trained at different spatial resolutions are [...] Read more.
A new approach for skeleton extraction has been designed to work directly with 3D point cloud data. It blends hierarchical segmentation with a multi-scale ensemble built on top of modified PointNet models. Outputs from three network variants trained at different spatial resolutions are aggregated using majority voting, unweighted averaging, and adaptive weighting, with the latter yielding the best performance. Each joint is set at the center of its part. A radius-based filter is used to remove any outliers, specifically, points that fall too far from where the joints are expected to be. When evaluated on benchmark datasets such as DFaust, CMU, Kids, and EHF, the model demonstrated strong segmentation accuracy (mIoU = 0.8938) and low joint localization error (MPJPE = 22.82 mm). The method generalizes well to an unseen dataset (DanceDB), maintaining strong performance across diverse body types and poses. Compared to benchmark methods such as L1-Medial, Pinocchio, and MediaPipe, our approach offers greater anatomical symmetry, joint completeness, and robustness in occluded or overlapping regions. Structural integrity is maintained by working directly with 3D data, without the need for 2D projections or medial-axis approximations. The visual assessment of DanceDB results indicates improved anatomical accuracy, even in the absence of quantitative comparison. The outcome supports practical applications in animation, motion tracking, and biomechanics. Full article
(This article belongs to the Special Issue Asymmetric and Symmetric in Deep Computer Vision and Generative Modeling)
►▼ Show Figures

Figure 1

45 pages, 14000 KiB  
Article
Automated Eye Disease Diagnosis Using a 2D CNN with Grad-CAM: High-Accuracy Detection of Retinal Asymmetries for Multiclass Classification
by Sameh Abd El-Ghany, Mahmood A. Mahmood and A. A. Abd El-Aziz
Symmetry 2025, 17(5), 768; https://doi.org/10.3390/sym17050768 - 15 May 2025
Viewed by 754
Abstract
Eye diseases (EDs), including glaucoma, diabetic retinopathy, and cataracts, are major contributors to vision loss and reduced quality of life worldwide. These conditions not only affect millions of individuals but also impose a significant burden on global healthcare systems. As the population ages [...] Read more.
Eye diseases (EDs), including glaucoma, diabetic retinopathy, and cataracts, are major contributors to vision loss and reduced quality of life worldwide. These conditions not only affect millions of individuals but also impose a significant burden on global healthcare systems. As the population ages and lifestyle changes increase the prevalence of conditions like diabetes, the incidence of EDs is expected to rise, further straining diagnostic and treatment resources. Timely and accurate diagnosis is critical for effective management and prevention of vision loss, as early intervention can significantly slow disease progression and improve patient outcomes. However, traditional diagnostic methods rely heavily on manual analysis of fundus imaging, which is labor-intensive, time-consuming, and subject to human error. This underscores the urgent need for automated, efficient, and accurate diagnostic systems that can handle the growing demand while maintaining high diagnostic standards. Current approaches, while advancing, still face challenges such as inefficiency, susceptibility to errors, and limited ability to detect subtle retinal asymmetries, which are critical early indicators of disease. Effective solutions must address these issues while ensuring high accuracy, interpretability, and scalability. This research introduces a 2D single-channel convolutional neural network (CNN) based on ResNet101-V2 architecture. The model integrates gradient-weighted class activation mapping (Grad-CAM) to highlight retinal asymmetries linked to EDs, thereby enhancing interpretability and detection precision. Evaluated on retinal Optical Coherence Tomography (OCT) datasets for multiclass classification tasks, the model demonstrated exceptional performance, achieving accuracy rates of 99.90% for four-class tasks and 99.27% for eight-class tasks. By leveraging patterns of retinal symmetry and asymmetry, the proposed model improves early detection and simplifies the diagnostic workflow, offering a promising advancement in the field of automated eye disease diagnosis. Full article
(This article belongs to the Special Issue Asymmetric and Symmetric in Deep Computer Vision and Generative Modeling)
►▼ Show Figures

Figure 1

30 pages, 6219 KiB  
Article
A Robust EfficientNetV2-S Classifier for Predicting Acute Lymphoblastic Leukemia Based on Cross Validation
by A. A. Abd El-Aziz, Mahmood A. Mahmood and Sameh Abd El-Ghany
Symmetry 2025, 17(1), 24; https://doi.org/10.3390/sym17010024 - 26 Dec 2024
Viewed by 1449
Abstract
This research addresses the challenges of early detection of Acute Lymphoblastic Leukemia (ALL), a life-threatening blood cancer particularly prevalent in children. Manual diagnosis of ALL is often error-prone, time-consuming, and reliant on expert interpretation, leading to delays in treatment. This study proposes an [...] Read more.
This research addresses the challenges of early detection of Acute Lymphoblastic Leukemia (ALL), a life-threatening blood cancer particularly prevalent in children. Manual diagnosis of ALL is often error-prone, time-consuming, and reliant on expert interpretation, leading to delays in treatment. This study proposes an automated binary classification model based on the EfficientNetV2-S architecture to overcome these limitations, enhanced with 5-fold cross-validation (5KCV) for robust performance. A novel aspect of this research lies in leveraging the symmetry concepts of symmetric and asymmetric patterns within the microscopic imagery of white blood cells. Symmetry plays a critical role in distinguishing typical cellular structures (symmetric) from the abnormal morphological patterns (asymmetric) characteristic of ALL. By integrating insights from generative modeling techniques, the study explores how asymmetric distortions in cellular structures can serve as key markers for disease classification. The EfficientNetV2-S model was trained and validated using the normalized C-NMC_Leukemia dataset, achieving exceptional metrics: 97.34% accuracy, recall, precision, specificity, and F1-score. Comparative analysis showed the model outperforms recent classifiers, making it highly effective for distinguishing abnormal white blood cells. This approach accelerates diagnosis, reduces costs, and improves patient outcomes, offering a transformative tool for early ALL detection and treatment planning. Full article
(This article belongs to the Special Issue Asymmetric and Symmetric in Deep Computer Vision and Generative Modeling)
►▼ Show Figures

Figure 1

Review

Jump to: Research

29 pages, 1184 KiB  
Review
AI-Driven Technology in Heart Failure Detection and Diagnosis: A Review of the Advancement in Personalized Healthcare
by Ikteder Akhand Udoy and Omiya Hassan
Symmetry 2025, 17(3), 469; https://doi.org/10.3390/sym17030469 - 20 Mar 2025
Cited by 2 | Viewed by 2725
Abstract
Artificial intelligence (AI) is playing a dominant role in advancing heart failure detection and diagnosis, significantly furthering personalized healthcare. This review synthesizes AI-driven innovations by examining methodologies, applications, and outcomes. We investigate the integration of machine learning algorithms, diverse datasets including electronic health [...] Read more.
Artificial intelligence (AI) is playing a dominant role in advancing heart failure detection and diagnosis, significantly furthering personalized healthcare. This review synthesizes AI-driven innovations by examining methodologies, applications, and outcomes. We investigate the integration of machine learning algorithms, diverse datasets including electronic health records (EHRs), medical records, imaging data, and clinical notes, deep learning models, and neural networks to enhance diagnostic accuracy. Key advancements include prediction models that leverage real-time data from wearable devices alongside state-of-the-art AI systems trained on patient data from hospitals and clinics. Notably, recent studies have reported diagnostic accuracies ranging from 86.7% to as high as 99.9%, with sensitivity and specificity values often exceeding 97%, underscoring the potential of these AI systems to improve early detection and clinical decision-making substantially. Our review further explores the impact of symmetry and asymmetry in model design, highlighting that symmetric architectures like U-Net offer computational efficiency and structured feature extraction. In contrast, asymmetric models improve the sensitivity to rare conditions and subtle clinical patterns. Incorporating these deep learning (DL) methods in anomaly detection and disease progression modeling further reinforces their positive impact on diagnostic accuracy and patient outcomes. Furthermore, this review identifies challenges in current AI applications, such as data quality, algorithmic transparency, model bias, and evaluation metrics, while outlining future research directions, including integrating generative models, hybrid architectures, and explainable AI techniques to optimize clinical practice. Full article
(This article belongs to the Special Issue Asymmetric and Symmetric in Deep Computer Vision and Generative Modeling)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop