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Algorithms
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Advanced Machine Learning Algorithms for Image Processing
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Advanced Machine Learning Algorithms for Image Processing

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (30 June 2025) | Viewed by 2000

Special Issue Editors

Dr. Sud Sudirman

E-Mail Website
Guest Editor
School of Computer Science and Mathematics, Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool L3 3AF, UK
Interests: image analysis; machine learning; internet of things; computer graphics
Prof. Dr. Friska Natalia

E-Mail Website
Co-Guest Editor
Information Systems Department, Faculty of Engineering and Informatics, Universitas Multimedia Nusantara, Tangerang, Banten 15810, Indonesia
Interests: deep learning and machine learning; systems design and analytics; logistics; E-commerce; computer programming; modeling & simulation; business intelligence; big data

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue of the Algorithms journal dedicated to ‘Advanced Machine Learning Algorithms for Image Processing’.

Today, we continue to witness tremendous intertwining advancements in both the machine learning and image processing fields, which have powered our progress in search of solutions of current real-world problems in healthcare, medicine, climate change mitigation, automation, and many other areas. With this Special Issue, we aim to showcase cutting-edge research that pushes the boundaries of these fields, offering novel solutions to complex problems and unveiling exciting opportunities for the future. We believe that this Special Issue would be a very good opportunity to highlight the results of your hard work in these fields. We seek high-quality papers in a range of related fields including, but not limited to, the following:

  • Deep learning architectures (e.g., CNNs, RNNs, GANs, or Transformer-based models) for feature extraction, recognition, and generation.
  • Transfer learning and few-shot learning for efficient training with limited labeled data.
  • Explainable AI and interpretability in image processing tasks to provide insights into decision-making processes.
  • Advances in image processing and computer vision tasks such as object detection and recognition, image segmentation, image classification, image restoration and enhancement, and image analysis.
  • Applications of machine learning and image processing in various domains such as medical imaging, remote sensing, autonomous vehicles, and art restoration.

Dr. Sud Sudirman
Guest Editor

Prof. Dr. Friska Natalia
Co-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. Algorithms 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 1800 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

  • machine learning
  • deep learning
  • image and video processing
  • image and video analysis
  • computer vision
  • computer-aided diagnosis

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

Published Papers (2 papers)

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Research

22 pages, 8021 KB  
Article
Multi-Task Semi-Supervised Approach for Counting Cones in Adaptive Optics Images
by Vidya Bommanapally, Amir Akhavanrezayat, Parvathi Chundi, Quan Dong Nguyen and Mahadevan Subramaniam
Algorithms 2025, 18(9), 552; https://doi.org/10.3390/a18090552 - 2 Sep 2025
Viewed by 373
Abstract
Counting and density estimation of cone cells using adaptive optics (AO) imaging plays an important role in the clinical management of retinal diseases. A novel deep learning approach for the cone counting task with minimal manual labeling of cone cells in AO images [...] Read more.
Counting and density estimation of cone cells using adaptive optics (AO) imaging plays an important role in the clinical management of retinal diseases. A novel deep learning approach for the cone counting task with minimal manual labeling of cone cells in AO images is described in this paper. We propose a hybrid multi-task semi-supervised learning (MTSSL) framework that simultaneously trains on unlabeled and labeled data. On the unlabeled images, the model learns structural and relational features by employing two self-supervised pretext tasks—image inpainting (IP) and learning-to-rank (L2R). At the same time, it leverages a small set of labeled examples to supervise a density estimation head for cone counting. By jointly minimizing the image reconstruction loss, the ranking loss, and the supervised density-map loss, our approach harnesses the rich information in unlabeled data to learn feature representations and directly incorporates ground-truth annotations to guide accurate density prediction and counts. Experiments were conducted on a dataset of AO images of 120 subjects captured using a device with a retinal camera (rtx1) with a wide field-of-view. MTSSL gains strengths from hybrid self-supervised pretext tasks of generative and predictive pretraining that aid in learning global and local context required for counting cones. The results show that the proposed MTSSL approach significantly outperforms the individual self-supervised pipelines with an RMSE score improved by a factor of 2 for cone counting. Full article
(This article belongs to the Special Issue Advanced Machine Learning Algorithms for Image Processing)
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24 pages, 2716 KB  
Article
Synthetic Data-Based Algorithm Selection for Medical Image Classification Under Limited Data Availability
by Maxim Zhabinets, Benjamin Tyler, Martin Lukac, Shinobu Nagayama, Ferdinand Molnár and Michitaka Kameyama
Algorithms 2025, 18(6), 310; https://doi.org/10.3390/a18060310 - 25 May 2025
Viewed by 416
Abstract
The Algorithm selection approach improves performance by dynamically choosing the optimal Algorithm for each input instance. While this selection strategy has been extensively studied, the amount of data and their nature have not yet been investigated with respect to meta-learning, particularly in scenarios [...] Read more.
The Algorithm selection approach improves performance by dynamically choosing the optimal Algorithm for each input instance. While this selection strategy has been extensively studied, the amount of data and their nature have not yet been investigated with respect to meta-learning, particularly in scenarios with limited data availability. This paper addresses a critical challenge: where additional data might not be available for training an Algorithm selector, and to implement a selection mechanism, data must be generated. Focusing on medical image classification, we investigate whether synthetic data can effectively train an Algorithm selector when real training data are scarce. Our methodology involves data generation using Generative Adversarial Network. To determine if Algorithm selection trained on synthetically generated data can achieve the same accuracy as if trained on real-world natural data, we systematically evaluate the data generative model using the smallest amount of data needed to choose the right Algorithm and to achieve the expected level of accuracy. Our experimental results demonstrate that using a small amount of real samples can provide enough information to a Generative Adversarial Network to synthesize a new dataset that, when used for training the Algorithm selection, improves image classification in some cases. Full article
(This article belongs to the Special Issue Advanced Machine Learning Algorithms for Image Processing)
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