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AI-Based Image Processing and Computer Vision, 2nd Edition
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AI-Based Image Processing and Computer Vision, 2nd Edition

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 466

Special Issue Editor

Prof. Dr. Kohei Arai

E-Mail Website
Guest Editor
Science and Engineering Faculty, Saga University, Saga 840-8502, Japan
Interests: artificial intelligence; big data analysis; computer vision; human-computer interaction; modeling and simulation; satellite remote sensing; image processing and analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, AI-based image processing and computer vision have made remarkable progress, and they are being put into practical use across various fields. Furthermore, technological advances in deep learning have significantly improved the accuracy of image recognition. It is now possible to perform tasks with high precision, such as human face recognition and object detection, which were difficult with conventional image recognition technology. Technological advances in generative models (such as GAN) have also made it possible to generate high-quality images that look as if they had been created by humans. This technology is used in fields such as image editing and advertising production. Moreover, image restoration technology removes noise from images and complements missing parts, and such technology that utilizes AI has been developed, thereby making it possible to restore more natural and high-quality images. Moreover, 3D recognition technology using 3D sensors and deep learning has progressed rapidly, and it is utilized in fields such as robotics and autonomous driving. Video analysis technology recognizes the movement of people and objects from videos, analyzes their actions, and is used in surveillance cameras and security systems. Meanwhile, augmented reality (AR) and virtual reality (VR), when combined with AI technology, can provide a more realistic and immersive experience. This technology is used in fields such as entertainment and education. In addition, AI technology is expected to generate new innovations by merging with other technologies such as robotics, autonomous driving, and medical care. However, ethical issues have also arisen with the development of AI. For example, if AI is misused, problems such as privacy invasion and discrimination may occur. It is important to respond appropriately to these issues. Accordingly, the following research areas are selected for this Special Issue: AI-based image processing and computer vision, pattern analysis, machine intelligence, pattern recognition, and image understanding. Your contributions would be highly appreciated.

Prof. Dr. Kohei Arai
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. Information 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

  • image processing
  • computer vision
  • pattern analysis
  • machine intelligence
  • pattern recognition
  • image understanding
 

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Published Papers (1 paper)

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Research

25 pages, 5618 KB  
Article
Evaluating the Generalisability of Convolutional Neural Networks for Diabetic Retinopathy Detection in Latin America and Sub-Saharan Africa
by Rogers Mwavu, Fred Kaggwa, Simon Arunga and William Wasswa
Information 2026, 17(6), 552; https://doi.org/10.3390/info17060552 - 3 Jun 2026
Viewed by 200
Abstract
Diabetic retinopathy is a leading cause of vision loss worldwide, particularly impacting individuals in low- and middle-income countries with limited healthcare access. Early detection through automated screening systems is essential for improving outcomes, as timely intervention can prevent severe vision impairment. However, most [...] Read more.
Diabetic retinopathy is a leading cause of vision loss worldwide, particularly impacting individuals in low- and middle-income countries with limited healthcare access. Early detection through automated screening systems is essential for improving outcomes, as timely intervention can prevent severe vision impairment. However, most of the available AI models have not been evaluated in low-resource settings. Hence, this study presents an evaluation of the efficacy of advanced deep learning architectures for detecting rDR across diverse population datasets. A dual-phase validation approach was employed to assess model performance. Internal validation utilised the BrSET dataset to establish baseline performance metrics, while external validation was conducted on the MoDRIA dataset, which encompasses various conditions and demographics, to evaluate model robustness. Key performance metrics, including accuracy, specificity, sensitivity, F1-score, and calibration scores, were systematically recorded and analysed. Internal validation revealed high accuracy across all models, EfficientNetB0 achieved the highest classification accuracy (0.9561; 95% CI 0.9490–0.9630), EfficientNetB3 demonstrated superior overall discriminative performance, achieving the highest AUROC (0.9892; 95% CI 0.9841–0.9934) highest sensitivity (0.9573), and lowest Brier score (0.0168). Meanwhile, DenseNet exhibited the most balanced clinical screening performance, achieving the highest F1-score (0.7259; 95% CI 0.6797–0.7669) and Youden Index (0.2381), indicating improved balance between sensitivity and specificity. In contrast, external validation revealed substantial deterioration in model performance across all architectures, highlighting major limitations in cross-population generalisability. Although EfficientNetB0 achieved the highest external accuracy (0.8821; 95% CI 0.8746–0.8898), AUROC values declined markedly across models (0.5140–0.6104), accompanied by poor sensitivity, reduced F1-scores, and substantial calibration instability. EfficientNetB3 achieved the highest external sensitivity (0.5939), whereas calibration analyses demonstrated unreliable probability estimation under domain-shift conditions. These findings suggest that AI models trained on geographically homogeneous retinal imaging datasets may not generalise reliably across underrepresented populations. Population differences and imaging variability substantially affected external model performance, highlighting the need for diverse datasets, rigorous external validation, and adaptive recalibration before clinical deployment of AI-driven DR screening systems. Full article
(This article belongs to the Special Issue AI-Based Image Processing and Computer Vision, 2nd Edition)
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