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Mathematics
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Machine Learning Applications in Image Processing and Computer Vision
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Machine Learning Applications in 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: 20 August 2025 | Viewed by 909

Special Issue Editor

Dr. Manuel G. Forero

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad de Ibagué, Ibagué 730002, Colombia
Interests: image processing; machine learning; pattern recognition; microscopy and biomedical image analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit papers to a Special Issue dedicated to advancing the synergy between mathematical modeling, analytical methods, and machine learning techniques in image processing and computer vision. This Special Issue aims to showcase innovative research that addresses pressing challenges in various domains, such as medical diagnostics, biological imaging, remote sensing, and satellite image analysis, among others.

We seek contributions that propose novel solutions to improve the robustness, accuracy, interpretability, and explainability of algorithms within these fields. Submissions may include, but are not limited to, work that combines mathematical modeling with machine learning to enhance image analysis workflows, develop better noise reduction or feature extraction techniques, or create new approaches for image segmentation, classification, and anomaly detection.

Furthermore, we are especially interested in research that pushes the boundaries of current knowledge, particularly in emerging areas such as reinforcement learning, synthetic image generation, domain adaptation, and transfer learning. We welcome studies that propose frameworks to tackle domain-specific image challenges or that demonstrate the potential of interdisciplinary approaches to overcome limitations in traditional methods.

I hope this Special Issue will inspire further exploration and collaboration, contributing to advancements that have real-world impacts.

Dr. Manuel G. Forero
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

  • mathematical modeling
  • machine learning
  • image processing
  • computer vision
  • interpretability
  • explainable AI
  • medical imaging
  • synthetic image generation
  • feature extraction
  • anomaly detection

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Published Papers (1 paper)

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Research

21 pages, 4055 KiB  
Article
Modified Whale Optimization Algorithm for Multiclass Skin Cancer Classification
by Abdul Majid, Masad A. Alrasheedi, Abdulmajeed Atiah Alharbi, Jeza Allohibi and Seung-Won Lee
Mathematics 2025, 13(6), 929; https://doi.org/10.3390/math13060929 - 11 Mar 2025
Viewed by 657
Abstract
Skin cancer is a major global health concern and one of the deadliest forms of cancer. Early and accurate detection significantly increases the chances of survival. However, traditional visual inspection methods are time-consuming and prone to errors due to artifacts and noise in [...] Read more.
Skin cancer is a major global health concern and one of the deadliest forms of cancer. Early and accurate detection significantly increases the chances of survival. However, traditional visual inspection methods are time-consuming and prone to errors due to artifacts and noise in dermoscopic images. To address these challenges, this paper proposes an innovative deep learning-based framework that integrates an ensemble of two pre-trained convolutional neural networks (CNNs), SqueezeNet and InceptionResNet-V2, combined with an improved Whale Optimization Algorithm (WOA) for feature selection. The deep features extracted from both models are fused to create a comprehensive feature set, which is then optimized using the proposed enhanced WOA that employs a quadratic decay function for dynamic parameter tuning and an advanced mutation mechanism to prevent premature convergence. The optimized features are fed into machine learning classifiers to achieve robust classification performance. The effectiveness of the framework is evaluated on two benchmark datasets, PH2 and Med-Node, achieving state-of-the-art classification accuracies of 95.48% and 98.59%, respectively. Comparative analysis with existing optimization algorithms and skin cancer classification approaches demonstrates the superiority of the proposed method in terms of accuracy, robustness, and computational efficiency. Our method outperforms the genetic algorithm (GA), Particle Swarm Optimization (PSO), and the slime mould algorithm (SMA), as well as deep learning-based skin cancer classification models, which have reported accuracies of 87% to 94% in previous studies. A more effective feature selection methodology improves accuracy and reduces computational overhead while maintaining robust performance. Our enhanced deep learning ensemble and feature selection technique can improve early-stage skin cancer diagnosis, as shown by these data. Full article
(This article belongs to the Special Issue Machine Learning Applications in Image Processing and Computer Vision)
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