Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.6
2.2
Journals
Mathematics
Special Issues
Mathematical Methods in Artificial Intelligence for Image Processing
share announcement

Mathematical Methods in Artificial Intelligence for Image Processing

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

Deadline for manuscript submissions: 31 January 2026 | Viewed by 407

Special Issue Editor

Prof. Dr. Humberto de Jesús Ochoa Domínguez

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico
Interests: convolutional neural nets; deep learning; hardware for real-time, image processing and analysis

Special Issue Information

Dear Colleagues,

During the last decade, Artificial Intelligence (AI) and image processing have undergone remarkable advancements, transforming a wide array of domains and leading to groundbreaking applications. The fusion of AI's cognitive capabilities and sophisticated image processing techniques has revolutionized industries, catalyzed innovation, and enhanced everyday life in numerous ways.

This Special Issue aims to provide an international platform for the swift publication of research from theoretical analysis to practical applications of AI in image processing across branches of engineering. Submitted papers should present novel AI techniques in image processing and applications to real-world engineering problems.

The topics of interest for publication include, but are not limited to, the following:

  • Real world applications;
  • Artificial intelligence techniques;
  • Image generation and enhancement;
  • Image processing and analysis;
  • Image recognition and classification;
  • Image reconstruction and restoration;
  • Medical and biomedical image analysis and understanding;
  • Object detection and segmentation;
  • Augmented reality;
  • Artificial intelligence in computer vision;
  • Real-time image and video processing;
  • Self-organizing and bio-inspired systems;
  • Biometrics and forensics;
  • Simulation and modelling;
  • Explainability for image data.

Prof. Dr. Humberto de Jesús Ochoa Domínguez
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. 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

  • artificial intelligence
  • deep learning
  • convolutional neural networks
  • image processing
  • image detection and recognition
  • medical image processing
  • image reconstruction
  • pattern recognition

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 (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

24 pages, 2159 KiB  
Article
Cross-Domain Transfer Learning Architecture for Microcalcification Cluster Detection Using the MEXBreast Multiresolution Mammography Dataset
by Ricardo Salvador Luna Lozoya, Humberto de Jesús Ochoa Domínguez, Juan Humberto Sossa Azuela, Vianey Guadalupe Cruz Sánchez, Osslan Osiris Vergara Villegas and Karina Núñez Barragán
Mathematics 2025, 13(15), 2422; https://doi.org/10.3390/math13152422 - 28 Jul 2025
Abstract
Microcalcification clusters (MCCs) are key indicators of breast cancer, with studies showing that approximately 50% of mammograms with MCCs confirm a cancer diagnosis. Early detection is critical, as it ensures a five-year survival rate of up to 99%. However, MCC detection remains challenging [...] Read more.
Microcalcification clusters (MCCs) are key indicators of breast cancer, with studies showing that approximately 50% of mammograms with MCCs confirm a cancer diagnosis. Early detection is critical, as it ensures a five-year survival rate of up to 99%. However, MCC detection remains challenging due to their features, such as small size, texture, shape, and impalpability. Convolutional neural networks (CNNs) offer a solution for MCC detection. Nevertheless, CNNs are typically trained on single-resolution images, limiting their generalizability across different image resolutions. We propose a CNN trained on digital mammograms with three common resolutions: 50, 70, and 100 μm. The architecture processes individual 1 cm2 patches extracted from the mammograms as input samples and includes a MobileNetV2 backbone, followed by a flattening layer, a dense layer, and a sigmoid activation function. This architecture was trained to detect MCCs using patches extracted from the INbreast database, which has a resolution of 70 μm, and achieved an accuracy of 99.84%. We applied transfer learning (TL) and trained on 50, 70, and 100 μm resolution patches from the MEXBreast database, achieving accuracies of 98.32%, 99.27%, and 89.17%, respectively. For comparison purposes, models trained from scratch, without leveraging knowledge from the pretrained model, achieved 96.07%, 99.20%, and 83.59% accuracy for 50, 70, and 100 μm, respectively. Results demonstrate that TL improves MCC detection across resolutions by reusing pretrained knowledge. Full article
(This article belongs to the Special Issue Mathematical Methods in Artificial Intelligence for Image Processing)
►▼ 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