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Mathematics
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Mathematics in Machine Learning-Based Image Processing with Their Applications
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Mathematics in Machine Learning-Based Image Processing with Their Applications

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 2463

Special Issue Editors

Dr. Joanna Czajkowska

E-Mail Website
Guest Editor
Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland
Interests: medical image processing and analysis; computer-aided diagnosis; supporting the skin diseases diagnosis; artificial intelligence and machine learning methods
Special Issues, Collections and Topics in MDPI journals
Dr. Jan Juszczyk

E-Mail Website
Guest Editor
Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland
Interests: medical image processing and analysis; computer-aided diagnosis; wound imaging and analysis; radiation dose management systems; X-ray microtomography investigations
Dr. Joanna Jaworek-Korjakowska

E-Mail Website
Guest Editor
Department of Automatic Control and Robotics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering , AGH University of Science and Technology, 30-059 Cracow, Poland
Interests: deep neural networks; machine learning; computer vision; AI; pattern recognition; medical imaging; dermoscopy; dermatology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,  

In recent years, we have observed the rapid development of artificial intelligence, especially deep learning architectures within the machine learning research area, being a part of Mathematical Sciences. It has resulted in the increased accuracy of novel solutions as observed in ImageNet challenges. Therefore, machine learning-based algorithms comprise multiple applications in the field of computer vision, especially image-processing tasks in areas that include medical image analysis, autonomous driving, robotic vision, and cybersecurity. These techniques are commonly used for image pre-processing, registration, detection, segmentation, classification, reconstruction, retrieval, and feature extraction. Being robust and repeatable, they appropriately support the scientist in his or her daily work.   

This Special Issue aims to collect all the works reflecting the latest developments in machine learning, emphasizing their usage in image analysis. We invite the authors to submit original articles addressing the significant issues and new concepts in the field of image processing. To broaden the perspectives of applying machine learning techniques to image analysis, we especially welcome the contributions that target explainable model development, including areas such as the ethics of AI, fairness, transparency, and human–AI collaboration research contributing to the understanding of AI decisions.   

Contributions are welcome on a theoretical and experimental level as well as practical applications.

Dr. Joanna Czajkowska
Dr. Jan Juszczyk
Dr. Joanna Jaworek-Korjakowska
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. 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

  • machine learning
  • deep learning
  • artificial intelligence
  • neural network interpretability
  • explainable artificial intelligence
  • fairness and transparency in deep neural network solutions
  • image segmentation
  • image classification
  • image adjustment
  • image filtering
  • medical imaging
  • image retrieval
  • image reconstruction
  • image quality
  • feature extraction
  • multi-scale image analysis
  • multi-modal image analysis
  • multi-spectral image analysis

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Published Papers (2 papers)

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Research

14 pages, 2697 KiB  
Article
An Improved Medical Image Classification Algorithm Based on Adam Optimizer
by Haijing Sun, Wen Zhou, Jiapeng Yang, Yichuan Shao, Lei Xing, Qian Zhao and Le Zhang
Mathematics 2024, 12(16), 2509; https://doi.org/10.3390/math12162509 - 14 Aug 2024
Viewed by 549
Abstract
Due to the complexity and illegibility of medical images, it brings inconvenience and difficulty to the diagnosis of medical personnel. To address these issues, an optimization algorithm called GSL(Gradient sine linear) based on Adam algorithm improvement is proposed in this paper, which introduces [...] Read more.
Due to the complexity and illegibility of medical images, it brings inconvenience and difficulty to the diagnosis of medical personnel. To address these issues, an optimization algorithm called GSL(Gradient sine linear) based on Adam algorithm improvement is proposed in this paper, which introduces gradient pruning strategy, periodic adjustment of learning rate, and linear interpolation strategy. The gradient trimming technique can scale the gradient to prevent gradient explosion, while the periodic adjustment of the learning rate and linear interpolation strategy adjusts the learning rate according to the characteristics of the sinusoidal function, accelerating the convergence while reducing the drastic parameter fluctuations, improving the efficiency and stability of training. The experimental results show that compared to the classic Adam algorithm, this algorithm can demonstrate better classification accuracy, the GSL algorithm achieves an accuracy of 78% and 75.2% on the MobileNetV2 network and ShuffleNetV2 network under the Gastroenterology dataset; and on the MobileNetV2 network and ShuffleNetV2 network under the Glaucoma dataset, an accuracy of 84.72% and 83.12%. The GSL optimizer achieved significant performance improvement on various neural network structures and datasets, proving its effectiveness and practicality in the field of deep learning, and also providing new ideas and methods for solving the difficulties in medical image recognition. Full article
(This article belongs to the Special Issue Mathematics in Machine Learning-Based Image Processing with Their Applications)
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21 pages, 12763 KiB  
Article
Research and Implementation of Denoising Algorithm for Brain MRIs via Morphological Component Analysis and Adaptive Threshold Estimation
by Buhailiqiemu Awudong, Paerhati Yakupu, Jingwen Yan and Qi Li
Mathematics 2024, 12(5), 748; https://doi.org/10.3390/math12050748 - 1 Mar 2024
Viewed by 1217
Abstract
The inevitable noise generated in the acquisition and transmission process of MRIs seriously affects the reliability and accuracy of medical research and diagnosis. The denoising effect for Rician noise, whose distribution is related to MR image signal, is not good enough. Furthermore, the [...] Read more.
The inevitable noise generated in the acquisition and transmission process of MRIs seriously affects the reliability and accuracy of medical research and diagnosis. The denoising effect for Rician noise, whose distribution is related to MR image signal, is not good enough. Furthermore, the brain has a complex texture structure and a small density difference between different parts, which leads to higher quality requirements for brain MR images. To upgrade the reliability and accuracy of brain MRIs application and analysis, we designed a new and dedicated denoising algorithm (named VST–MCAATE), based on their inherent characteristics. Comparative experiments were performed on the same simulated and real brain MR datasets. The peak signal-to-noise ratio (PSNR), and mean structural similarity index measure (MSSIM) were used as objective image quality evaluation. The one-way ANOVA was used to compare the effects of denoising between different approaches. p < 0.01 was considered statistically significant. The experimental results show that the PSNR and MSSIM values of VST–MCAATE are significantly higher than state-of-the-art methods (p < 0.01), and also that residual images have no anatomical structure. The proposed denoising method has advantages in improving the quality of brain MRIs, while effectively removing the noise with a wide range of unknown noise levels without damaging texture details, and has potential clinical promise. Full article
(This article belongs to the Special Issue Mathematics in Machine Learning-Based Image Processing with Their Applications)
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