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Advances in Mathematical Analysis and Metaheuristic Optimization Techniques for Image...
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Advances in Mathematical Analysis and Metaheuristic Optimization Techniques for Image Processing and Computer Vision

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 525

Special Issue Editor

Prof. Dr. Alma Nayeli Rodríguez-Vázquez

E-Mail Website
Guest Editor
Department of Computer Science, University of Guadalajara, CUCEI, Blvd. Marcelino García Barragán 1421, Guadalajara 44430, Mexico
Interests: metaheuristic algorithms; artificial intelligence; image processing

Special Issue Information

Dear Colleagues,

Image processing and computer vision involve a wide range of challenging problems that are formulated as complex optimization tasks, often characterized by high dimensionality, nonlinearity, and multiple constraints. Metaheuristic optimization techniques, inspired by natural, biological, and social processes, have emerged as powerful and flexible tools to address these challenges, providing effective solutions where classical optimization methods may struggle.

The special issue “Advances in Mathematical Analysis and Metaheuristic Optimization Techniques for Image Processing and Computer Vision” aims to collect original research that highlights recent theoretical developments, algorithmic innovations, and practical applications of metaheuristic methods in visual computing. We welcome contributions that propose new metaheuristic algorithms, hybrid and adaptive strategies, and improved performance through parameter tuning or algorithmic enhancements.

Submissions may focus on a broad range of image processing and computer vision tasks, including, but not limited to, image enhancement, restoration, segmentation, feature extraction, object detection, recognition, and video analysis. Papers that integrate metaheuristic optimization with machine learning or deep learning models, as well as comparative studies and real-world applications in areas such as medical imaging, remote sensing, and autonomous systems, are particularly encouraged. This special issue seeks to provide a comprehensive and up-to-date perspective on the role of metaheuristic optimization in advancing image processing and computer vision research.

Prof. Dr. Alma Nayeli Rodríguez-Vázquez
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 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. Mathematics 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

  • metaheuristic optimization
  • image processing
  • computer vision
  • hybrid algorithms
  • visual computing

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Published Papers (1 paper)

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Research

25 pages, 3283 KB  
Article
Density-Aware Multi-Dataset Evaluation of Deep Learning for Mammographic Mass Detection and BI-RADS Classification
by Hector E. Zepeda-Reyes, Hayde Peregrina-Barreto and Gabriela C. Lopez-Armas
Mathematics 2026, 14(12), 2080; https://doi.org/10.3390/math14122080 - 10 Jun 2026
Viewed by 224
Abstract
Breast density has a significant impact on how clearly masses appear in mammography. It can also introduce bias in automatic localization systems when density distributions are uneven. Although advances in deep learning-based detection methods have been made, most studies report overall performance without [...] Read more.
Breast density has a significant impact on how clearly masses appear in mammography. It can also introduce bias in automatic localization systems when density distributions are uneven. Although advances in deep learning-based detection methods have been made, most studies report overall performance without explicitly accounting for variability associated with breast density. Breast cancer diagnosis from mammography is strongly influenced by dataset composition, annotation variability, and breast density distribution, factors that are rarely controlled in current AI evaluations. We introduce Mass-Bench, a clinically balanced and harmonized multi-dataset benchmark that integrates CBIS-DDSM, INBREAST, VINDr-Mammo, and DMID under a unified canonical schema, with standardized ACR density and BI-RADS encoding. Using a leakage-controlled and distribution-aware evaluation protocol, density-stratified mass detection and lesion-centered regions of interest (ROIs) classification were assessed across datasets. YOLO-based detection models achieved peak area under the curve (AUC) values up to 0.943; however, performance systematically degraded with increasing ACR density, revealing limitations that are often masked in imbalanced evaluations. By enforcing clinically representative density distributions, Mass-Bench provides a more reliable estimation of localization performance, which directly impacts downstream clinical tasks. In this context, binary ACR classification achieved F1-scores up to 0.976, while binary BI-RADS discrimination reached accuracies up to 0.93. However, multi-class classification remained more challenging, showing increased sensitivity to dataset heterogeneity and contextual information. These findings demonstrate that conventional evaluations may overestimate robustness, particularly in dense breast categories, and highlight the importance of density-aware benchmarking for developing reliable and clinically applicable AI systems in mammography. Full article
(This article belongs to the Special Issue Advances in Mathematical Analysis and Metaheuristic Optimization Techniques for Image Processing and Computer Vision)
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