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Advances in AI-Driven Computational Modeling for Image Processing

Submission Deadline: 30 April 2025 (closed) View: 460 Submit to Special Issue

Guest Editors

Dr. Sathishkumar V E

Email: sathishv@sunway.edu.my

Affiliation: Department of Computing and Information Systems, Sunway University, Malaysia

Homepage:

Research Interests: Data Mining, Machine Learning, Quantum Computing

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Dr. R. Karthik

Email: r.karthik@vit.ac.in

Affiliation: Centre for Cyber Physical Systems, Vellore Institute of Technology, India

Homepage:

Research Interests: Medical image processing, Computer Vision, Healthcare

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Summary

The special issue on "Advances in AI-Driven Computational Modeling for Image Processing" aims to provide a comprehensive platform for researchers and practitioners to discuss the latest advancements, challenges, and future directions in the integration of artificial intelligence (AI) with computational modeling techniques for image processing applications. This special issue seeks to highlight innovative approaches and methodologies that leverage AI to enhance image processing tasks such as image recognition, segmentation, restoration, enhancement, and understanding.

The objectives of this special issue are to:

1. Present state-of-the-art research on AI-driven computational modeling techniques for image processing.

2. Explore novel algorithms and frameworks that integrate AI with image processing applications.

3. Discuss real-world applications and case studies demonstrating the effectiveness of AI in image processing.

4. Identify current challenges and future research directions in the field.

We invite original research papers, review articles, and case studies on topics including, but not limited to:

· Deep learning architectures for image processing

· AI-driven image segmentation and object detection

· Image enhancement and restoration using AI techniques

· Computational modeling for medical image analysis

· AI-based image synthesis and generation

· Real-time image processing using AI

· AI in remote sensing and satellite image processing

· AI-driven techniques for image compression and coding

· Explainable AI in image processing

· Benchmarking and evaluation of AI models for image processing

· Ethical and societal implications of AI in image processing

Published Papers

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Open Access

ARTICLE

EffNet-CNN: A Semantic Model for Image Mining & Content-Based Image Retrieval
Rajendran Thanikachalam, Anandhavalli Muniasamy, Ashwag Alasmari, Rajendran Thavasimuthu
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.063063
（This article belongs to the Special Issue: Advances in AI-Driven Computational Modeling for Image Processing)
Abstract Content-Based Image Retrieval (CBIR) and image mining are becoming more important study fields in computer vision due to their wide range of applications in healthcare, security, and various domains. The image retrieval system mainly relies on the efficiency and accuracy of the classification models. This research addresses the challenge of enhancing the image retrieval system by developing a novel approach, EfficientNet-Convolutional Neural Network (EffNet-CNN). The key objective of this research is to evaluate the proposed EffNet-CNN model’s performance in image classification, image mining, and CBIR. The novelty of the proposed EffNet-CNN model includes the integration… More >

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ARTICLE

Integrating Speech-to-Text for Image Generation Using Generative Adversarial Networks
Smita Mahajan, Shilpa Gite, Biswajeet Pradhan, Abdullah Alamri, Shaunak Inamdar, Deva Shriyansh, Akshat Ashish Shah, Shruti Agarwal
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.058456
（This article belongs to the Special Issue: Advances in AI-Driven Computational Modeling for Image Processing)
Abstract The development of generative architectures has resulted in numerous novel deep-learning models that generate images using text inputs. However, humans naturally use speech for visualization prompts. Therefore, this paper proposes an architecture that integrates speech prompts as input to image-generation Generative Adversarial Networks (GANs) model, leveraging Speech-to-Text translation along with the CLIP + VQGAN model. The proposed method involves translating speech prompts into text, which is then used by the Contrastive Language-Image Pretraining (CLIP) + Vector Quantized Generative Adversarial Network (VQGAN) model to generate images. This paper outlines the steps required to implement such a… More >

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Open Access

ARTICLE

A Nature-Inspired AI Framework for Accurate Glaucoma Diagnosis
Jahanzaib Latif , Ahsan Wajahat, Alishba Tahir, Anas Bilal, Mohammed Zakariah, Abeer Alnuaim
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 539-567, 2025, DOI:10.32604/cmes.2025.062301
（This article belongs to the Special Issue: Advances in AI-Driven Computational Modeling for Image Processing)
Abstract Glaucoma, a leading cause of blindness, demands early detection for effective management. While AI-based diagnostic systems are gaining traction, their performance is often limited by challenges such as varying image backgrounds, pixel intensity inconsistencies, and object size variations. To address these limitations, we introduce an innovative, nature-inspired machine learning framework combining feature excitation-based dense segmentation networks (FEDS-Net) and an enhanced gray wolf optimization-supported support vector machine (IGWO-SVM). This dual-stage approach begins with FEDS-Net, which utilizes a fuzzy integral (FI) technique to accurately segment the optic cup (OC) and optic disk (OD) from retinal images, even More >

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148
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ARTICLE

Coupling the Power of YOLOv9 with Transformer for Small Object Detection in Remote-Sensing Images
Mohammad Barr
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 593-616, 2025, DOI:10.32604/cmes.2025.062264
（This article belongs to the Special Issue: Advances in AI-Driven Computational Modeling for Image Processing)
Abstract Recent years have seen a surge in interest in object detection on remote sensing images for applications such as surveillance and management. However, challenges like small object detection, scale variation, and the presence of closely packed objects in these images hinder accurate detection. Additionally, the motion blur effect further complicates the identification of such objects. To address these issues, we propose enhanced YOLOv9 with a transformer head (YOLOv9-TH). The model introduces an additional prediction head for detecting objects of varying sizes and swaps the original prediction heads for transformer heads to leverage self-attention mechanisms. We… More >

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139
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ARTICLE

Integrating Bayesian and Convolution Neural Network for Uncertainty Estimation of Cataract from Fundus Images
Anandhavalli Muniasamy, Ashwag Alasmari
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 569-592, 2025, DOI:10.32604/cmes.2025.060484
（This article belongs to the Special Issue: Advances in AI-Driven Computational Modeling for Image Processing)
Abstract The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of patients. Today, the mass disease that needs attention in this context is cataracts. Although deep learning has significantly advanced the analysis of ocular disease images, there is a need for a probabilistic model to generate the distributions of potential outcomes and thus make decisions related to uncertainty quantification. Therefore, this study implements a Bayesian Convolutional Neural Networks (BCNN) model for predicting cataracts by assigning probability values to the predictions. It prepares convolutional neural network (CNN) and BCNN models. More >
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ARTICLE

An Enhanced Lung Cancer Detection Approach Using Dual-Model Deep Learning Technique
Sumaia Mohamed Elhassan, Saad Mohamed Darwish, Saleh Mesbah Elkaffas
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 835-867, 2025, DOI:10.32604/cmes.2024.058770
（This article belongs to the Special Issue: Advances in AI-Driven Computational Modeling for Image Processing)
Abstract Lung cancer continues to be a leading cause of cancer-related deaths worldwide, emphasizing the critical need for improved diagnostic techniques. Early detection of lung tumors significantly increases the chances of successful treatment and survival. However, current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue. Single-model deep learning technologies for lung cancer detection, while beneficial, cannot capture the full range of features present in medical imaging data, leading to incomplete or inaccurate detection. Furthermore, it may not be robust enough to handle the… More >

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Advances in AI-Driven Computational Modeling for Image Processing

Guest Editors

Summary

Published Papers

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148

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55

View

351

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147

Cited by

3

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301

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148

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290

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139

View

279

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122

View

815

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448

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