Computers & Electrical Engineering最新文献_第2页

A bibliometric analysis of Homomorphic Encryption for privacy-preserving biometrics 保护隐私生物特征的同态加密文献计量学分析

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2026-01-17 DOI: 10.1016/j.compeleceng.2026.110969

Shreyansh Sharma , Anurag Mudgil , Richa Dubey , Anil Saini , Santanu Chaudhury

In recent years, biometric systems have become integral to authentication, access control, and identification. However, the sensitive nature of biometric data raises significant privacy concerns. Homomorphic Encryption (HE) has emerged as a promising solution, allowing computations on encrypted data without decryption, thus preserving privacy. This bibliometric survey provides a focused bibliometric analysis based on the Scopus dataset, highlighting the evolution and current state-of-the-art in HE techniques within the context of privacy-preserving biometrics. Key aspects explored include foundational principles, encryption schemes, biometric applications, and the patent landscape. The study analyzes 206 documents using bibliometric methods such as keyword co-occurrence networks, author co-citation analysis, thematic evolution, and Sankey diagrams. The findings highlight a notable increase in research and patent activity, with 30 publications and 12 patents in the past year alone, reflecting growing interest in the convergence of HE and biometrics. Emerging applications in Artificial Intelligence and Blockchain are identified, while potential future directions include healthcare, Industry 5.0, and the Metaverse. This survey offers valuable insights into current research trends, challenges, and future opportunities, contributing to the advancement of privacy-preserving technologies in biometric systems.

近年来，生物识别系统已成为认证、访问控制和身份识别的重要组成部分。然而，生物特征数据的敏感性引发了重大的隐私问题。同态加密（HE）已经成为一种很有前途的解决方案，允许在不解密的情况下对加密数据进行计算，从而保护隐私。这项文献计量调查提供了一个基于Scopus数据集的重点文献计量分析，突出了在保护隐私的生物计量背景下HE技术的发展和当前的最新技术。探讨的关键方面包括基本原理、加密方案、生物识别应用和专利景观。本研究利用关键词共现网络、作者共被引分析、主题演变和Sankey图等文献计量学方法对206篇文献进行分析。这些发现突出了研究和专利活动的显著增加，仅在过去一年就有30篇出版物和12项专利，反映了人们对HE和生物识别技术融合的兴趣日益浓厚。确定了人工智能和区块链中的新兴应用，而潜在的未来方向包括医疗保健、工业5.0和元宇宙。这项调查对当前的研究趋势、挑战和未来的机会提供了有价值的见解，有助于生物识别系统中隐私保护技术的进步。

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引用次数: 0

Design of lightweight image encryption scheme for saliency protection in autonomous control systems 自主控制系统中图像显著性保护的轻量级加密方案设计

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2026-01-17 DOI: 10.1016/j.compeleceng.2026.110966

Lal Said , Muhammad Amin

Autonomous and remotely operated systems rely on image data for critical decision-making, yet these images are often sent over insecure channels, making them vulnerable to interception or tampering. This paper presents a lightweight image encryption scheme that uses Substitution–Permutation Network architecture with modular arithmetic-based block permutation and dynamically generated chaos-driven substitution boxes. The scheme employs dual key-dependent substitution and exclusive OR operations, ensuring that even a single-bit key change produce a completely different encrypted output. Security analysis shows a large key space, strong resistance to brute force attacks, high entropy, and desirable statistical properties. The proposed method achieves higher throughput than conventional ciphers while preserving salient image content even under pixel loss. These results demonstrate that the scheme provides secure and efficient image protection for resource-constrained environments.

自主和远程操作系统依赖图像数据进行关键决策，然而这些图像通常通过不安全的通道发送，使其容易被拦截或篡改。本文提出了一种轻量级的图像加密方案，该方案采用基于模块化算法的块置换和动态生成的混沌驱动替换盒的替换置换网络体系结构。该方案采用双密钥依赖替换和排他或操作，确保即使是单个密钥更改也会产生完全不同的加密输出。安全性分析显示密钥空间大、抗暴力攻击能力强、高熵和理想的统计特性。该方法比传统密码实现了更高的吞吐量，即使在像素丢失的情况下也能保持显著的图像内容。结果表明，该方案为资源受限环境提供了安全有效的图像保护。

引用次数: 0

A novel hybrid cheetah dung beetle optimization algorithm to solve cloud-fog scheduling problems 一种新的混合猎豹屎壳郎优化算法求解云雾调度问题

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2026-01-17 DOI: 10.1016/j.compeleceng.2026.110968

Rakesh Reddy Gurrala, Sampath Kumar Tallapally

The Internet of Things (IoT) revolution has resulted in massive data generation, requiring effective processing. Due to their proximity, tasks that demand a prompt response are sent to the fog node. In contrast, complex tasks are transferred to the cloud due to its massive processing capacity. Transferring tasks to the fog reduces the transmission latency while increasing energy consumption. In contrast, moving work to the cloud lowers energy consumption but increases transmission latency owing to long distances. Therefore, to balance the trade-offs between energy consumption and transmission delay, a hybrid Cheetah Dung Beetle Optimization Algorithm (CDBOA) based job scheduling strategy is used in this work. This hybrid algorithm balances local exploitation and global exploration by integrating the dung beetle optimization algorithm (DBOA) with the cheetah optimization algorithm (COA). This methodology effectively assigns jobs to fog and cloud resources according to their processing requirements and delay sensitivity, guaranteeing effective processing and energy conservation. The effectiveness of the proposed method has been evaluated using NASA iPSC and HPC2N workloads. The results show that the recommended approach performs better than other methods, with 12.64%, 27.60%, 21.55%, and 10.16% improvements for makespan, energy consumption, cost and delay, demonstrating the robustness of the suggested method.

物联网（IoT）革命导致了大量数据的产生，需要有效的处理。由于它们的接近性，需要快速响应的任务被发送到雾节点。相比之下，复杂的任务由于其庞大的处理能力而转移到云上。将任务转移到雾中减少了传输延迟，同时增加了能量消耗。相比之下，将工作转移到云端可以降低能耗，但由于距离较远，会增加传输延迟。因此，为了平衡能量消耗和传输延迟之间的平衡，本文采用了一种基于混合猎豹屎壳虫优化算法（CDBOA）的作业调度策略。该算法将屎壳虫优化算法（DBOA）与猎豹优化算法（COA）相结合，平衡了局部开发与全局探索。该方法根据雾和云资源的处理需求和延迟敏感性，有效地为雾和云资源分配任务，保证了有效的处理和节能。采用NASA iPSC和HPC2N工作负载对所提出方法的有效性进行了评估。结果表明，该方法在完工时间、能耗、成本和延迟方面的性能分别提高了12.64%、27.60%、21.55%和10.16%，证明了该方法的鲁棒性。

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引用次数: 0

Hierarchical mobile-dense convolutional architecture for tampered image detection using focal optimization with quantized edge TPU deployment 采用量化边缘TPU部署的焦点优化的分层移动密集卷积结构篡改图像检测

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2026-01-16 DOI: 10.1016/j.compeleceng.2026.110979

Badam Shanmukha Venkata Vinayak , Rama Muni Reddy Yanamala , Rayappa David Amar Raj , Archana Pallakonda

The availability of powerful digital editing tools has made image tampering increasingly sophisticated, posing significant challenges to journalism, forensics, and social media authenticity. To address the limitations of conventional and transformer-based forgery detection approaches – which often suffer from feature redundancy, compressibility instability, and high computational demands – this study introduces a deep learning architecture for tampered image detection. The model integrates a MobileNetV2-based encoder for compact spatial feature extraction, multi-scale hierarchical feature reuse blocks inspired by DenseNet, and a U-Net-type decoder for precise forgery localization. Class imbalance is mitigated using an enhanced binary classifier with focal loss. The entire model is quantized and deployed on a Google Coral Edge TPU, achieving real-time classification performance (approximately 135 ms per image) in low-power, resource-limited environments. The model is trained and tested on four benchmark forgery datasets – CASIA v1, Columbia, MICC-F2000, and Defacto-Splicing – and demonstrates excellent results: AUC = 1.00 and accuracy = 99% on Defacto, AUC = 0.967 and F1-score = 0.915 on Columbia, and strong performance on both high-resolution (MICC-F2000) and compressed (CASIA v1) datasets. Comparative analyses show that the proposed approach outperforms recent CNN- and Transformer-based methods while using only 5.7 million parameters, confirming its efficacy, scalability, and suitability for embedded AI systems. Thus, the proposed method represents a lightweight, hardware-deployable, and interpretable solution for robust image forgery detection.

强大的数字编辑工具的可用性使得图像篡改变得越来越复杂，对新闻业、法医学和社交媒体的真实性构成了重大挑战。为了解决传统的和基于变压器的伪造检测方法的局限性——通常存在特征冗余、可压缩性不稳定和高计算需求——本研究引入了一种用于篡改图像检测的深度学习架构。该模型集成了基于mobilenetv2的编码器，用于紧凑的空间特征提取，受DenseNet启发的多尺度分层特征重用块，以及u - net类型的解码器，用于精确的伪造定位。类失衡是减轻使用增强的二元分类器与焦点损失。整个模型被量化并部署在谷歌Coral Edge TPU上，在低功耗、资源有限的环境中实现实时分类性能（每张图像约135毫秒）。该模型在四个基准伪造数据集（CASIA v1、Columbia、mic - f2000和Defacto- splicing）上进行了训练和测试，并展示了出色的结果：Defacto上的AUC = 1.00，准确率= 99%，Columbia上的AUC = 0.967, F1-score = 0.915，在高分辨率（mic - f2000）和压缩（CASIA v1）数据集上都表现出色。对比分析表明，所提出的方法在仅使用570万个参数的情况下优于最近基于CNN和transformer的方法，证实了其有效性、可扩展性和对嵌入式人工智能系统的适用性。因此，所提出的方法代表了一种轻量级、硬件可部署和可解释的鲁棒图像伪造检测解决方案。

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引用次数: 0

Optimized 2-D FIR filter bank architecture using various symmetries with parallel processing and DA 利用并行处理和数据处理优化了二维FIR滤波器组结构

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2026-01-16 DOI: 10.1016/j.compeleceng.2026.110941

Venkata Krishna Odugu , P. Ramakrishna , T. Vasudeva Reddy , G Harish Babu , Janardhanarao S

In this study, a new Filter Bank (FB) architecture for a 2-D FIR filter and implementation in VLSI design with the help of symmetric processing, parallelism, and Distributed Arithmetic (DA) ideas are presented. This work is motivated by the need for hardware-efficient 2D FIR filter architectures that reduce computational complexity, Power Consumption (PC), and resource usage in real-time image processing applications. Parallel processing is incorporated into the design to boost throughput and to decrease the quantity of multipliers, symmetry is introduced into the coefficients of the filter. In place of the remaining multipliers, Dual Port-Look-Up Table (DP-LUT)-based DA are proposed to reduce the area and power. Four types of symmetries are considered, and each architecture is explored and implemented using the proposed DA approach. Finally, all these filter structures are integrated by considering a common memory module and a control logic. Memory reuse and sharing are made possible by the FB design, which also allows for parallel processing. The suggested FB design has low resource requirements in terms of both memory and processing power. The hardware utilization synthesis summary is assessed for the target device of the Field Programmable Gate Array (FPGA). After that, the design is synthesized in 45 nm CMOS technology using Cadence's Genus tools for ASIC design. Existing 2-D FIR filter designs and traditional multiplier-based filter architectures are analyzed in terms of area, latency, and PC reports. The proposed FB architecture achieves up to 98.04% reduction in ADP and up to 64.51% reduction in PDP compared to existing designs, highlighting its efficiency in both area and power optimization. The proposed work's layout is then provided, including the Innovus tools used to determine the place and route.

在本研究中，提出了一种新的用于二维FIR滤波器的滤波器组（FB）架构，并利用对称处理、并行性和分布式算法（DA）思想在VLSI设计中实现。这项工作的动机是需要硬件高效的2D FIR滤波器架构，以降低实时图像处理应用中的计算复杂性、功耗（PC）和资源使用。为了提高吞吐量和减少乘法器的数量，设计中引入了并行处理，并在滤波器系数中引入了对称性。采用双端口查找表（Dual port - lookup Table, DP-LUT）代替剩余的乘法器来减少面积和功耗。考虑了四种类型的对称性，并使用所提出的数据处理方法探索和实现了每种体系结构。最后，通过考虑公共存储模块和控制逻辑，将所有这些滤波器结构集成在一起。FB设计使得内存重用和共享成为可能，它还允许并行处理。建议的FB设计在内存和处理能力方面具有较低的资源需求。对现场可编程门阵列（FPGA）目标器件的硬件利用率进行了综合评价。之后，使用Cadence的Genus工具进行ASIC设计，在45 nm CMOS技术中进行设计合成。从面积、延迟和PC报告等方面分析了现有的二维FIR滤波器设计和传统的基于乘法器的滤波器架构。与现有设计相比，所提出的FB架构实现了高达98.04%的ADP降低和高达64.51%的PDP降低，突出了其在面积和功耗优化方面的效率。然后提供建议的工作布局，包括用于确定地点和路线的Innovus工具。

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引用次数: 0

Comprehensive analysis of the state of art on emotion recognition using EEG 基于脑电图的情绪识别研究现状综合分析

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2026-01-16 DOI: 10.1016/j.compeleceng.2026.110958

Anju Mishra , Priya Ranjan

Emotion recognition from physiological signals is an emerging field due to its vast application areas. The electroencephalogram (EEG) as a physiological marker in developing automated emotion recognition systems is gaining popularity with its ability to capture the brain's electrical activity providing a window into understanding how these emotional states are represented and processed. Because of this inherent capability of EEG recordings, this systematic review intends to give the readers a comprehensive understanding of the state of the art of the emotion recognition domain and the tools and technologies used by other contemporary researchers in this field. The review outlines the latest research in the field and also performs a comprehensive analysis of available literature to identify the best tools and technologies used by researchers in the domain at every step of the development of such models. The final section of the review tries to point out some directions that can be worked out in the future by the researchers.

基于生理信号的情绪识别是一个新兴的领域，有着广阔的应用领域。脑电图（EEG）作为开发自动情绪识别系统的一种生理标记物越来越受欢迎，因为它能够捕捉大脑的电活动，为理解这些情绪状态是如何表征和处理的提供了一个窗口。由于脑电图记录的这种固有能力，本系统综述旨在让读者全面了解情绪识别领域的最新技术，以及该领域其他当代研究人员使用的工具和技术。该综述概述了该领域的最新研究，并对现有文献进行了全面分析，以确定该领域研究人员在开发此类模型的每一步中使用的最佳工具和技术。评论的最后一部分试图指出研究人员未来可以制定的一些方向。

引用次数: 0

A review of multimodal sentiment analysis: Taxonomy, issues, challenges, and future perspectives 多模态情感分析综述：分类、问题、挑战和未来展望

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2026-01-16 DOI: 10.1016/j.compeleceng.2026.110959

Khalid Anwar, Shreya, Meghna Sharma, Kritika Saanvi

Recent developments in computational intelligence have produced a huge volume of multimodal data across different digital platforms. This data is a great source of contextual, sentimental, and emotional information. Multimodal sentiment analysis (MMSA) is the process of inferring sentiments from multimodal data. MMSA has improved the effectiveness and accuracy of sentiment analysis by integrating heterogeneous modalities. However, there are several issues and challenges in combining multiple modalities, like high complexity, modality fusion, lack of explainability, and temporal synchronization. This paper presents a review of MMSA, discussing data modalities, fusion approaches, issues and challenges. It also presents the statistical analysis and overview of datasets and evaluation metrics used in the reviewed papers. Moreover, it identifies several future research opportunities for the research advancements in MMSA. It is believed that the article will be beneficial for the researchers working in the relevant field.

计算智能的最新发展已经在不同的数字平台上产生了大量的多模态数据。这些数据是上下文、情感和情感信息的重要来源。多模态情感分析（MMSA）是从多模态数据中推断情感的过程。MMSA通过整合异构模式提高了情感分析的有效性和准确性。然而，在多模态组合中存在一些问题和挑战，如高复杂性、模态融合、缺乏可解释性和时间同步。本文介绍了MMSA的综述，讨论了数据模式、融合方法、问题和挑战。它还介绍了数据集的统计分析和概述，以及在审查论文中使用的评估指标。此外，它还确定了MMSA研究进展的几个未来研究机会。相信本文将对相关领域的研究人员有所裨益。

引用次数: 0

A game-theoretic approach to fair and grid-aware load flexibility allocation in residential distribution networks 基于博弈论的居民配电网负荷柔性公平分配方法

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2026-01-15 DOI: 10.1016/j.compeleceng.2026.110976

Gabriel Gómez-Ruiz, Jesús Clavijo-Camacho, Reyes Sánchez-Herrera, José M. Andújar

This article evaluates the potential of thermostatically controlled loads (TCL) as flexible resources to improve power quality―particularly phase unbalance―in low-voltage residential distribution networks while ensuring fair consumer participation. To address both grid-level and social objectives, the adaptive fairness and grid-aware allocation (AFGA) algorithm is proposed. This algorithm integrates cooperative game theory and Nash bargaining principles to jointly optimize phase balancing and consumer utility. The proposed approach dynamically allocates residential consumer flexibility by accounting for phase-level constraints, individual flexibility capacity, and historical participation, thereby preventing the persistent overuse of specific consumers and promoting equitable long-term engagement. Simulation results on a representative residential network with 100 households demonstrate that, with only 20% participation, the AFGA algorithm reduces the unbalance load factor (ULF) to below 10%, achieves a highly equitable distribution of benefits (Gini index = 0.065), and effectively enforces adaptive fairness through penalty-feedback mechanisms. Furthermore, the algorithm completes a full-day simulation in 102 s with only 0.24 MB of peak memory usage. These findings position the AFGA algorithm as an effective and scalable solution for integrating fairness-aware residential flexibility into the operation of low-voltage residential distribution networks.

本文评估了恒温控制负载（TCL）作为灵活资源的潜力，以改善低压住宅配电网的电力质量，特别是相位不平衡，同时确保公平的消费者参与。为了同时解决网格级和社会级目标，提出了自适应公平和网格感知分配（AFGA）算法。该算法结合合作博弈理论和纳什议价原则，共同优化阶段平衡和消费者效用。该方法通过考虑阶段约束、个人灵活能力和历史参与来动态分配住宅消费者的灵活性，从而防止特定消费者的持续过度使用，促进公平的长期参与。在100户代表性居民网络上的仿真结果表明，在参与率仅为20%的情况下，AFGA算法将不平衡负荷因子（ULF）降低到10%以下，实现了高度公平的利益分配（基尼系数= 0.065），并通过惩罚反馈机制有效地实现了自适应公平。此外，该算法在102秒内完成全天模拟，峰值内存使用仅为0.24 MB。这些发现将AFGA算法定位为一种有效且可扩展的解决方案，用于将公平意识的住宅灵活性整合到低压住宅配电网的运行中。

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引用次数: 0

A reversible image steganography framework against gradient inversion attacks via saliency-guided embedding 一种基于显著性嵌入的抗梯度反转攻击的可逆图像隐写框架

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2026-01-15 DOI: 10.1016/j.compeleceng.2026.110951

Chen Liang , Yuxin Zhou , Ziqi Wang , Jiamin Zheng

With the widespread application of edge collaborative inference, concerns regarding data privacy and model interpretability are increasingly prominent. Gradient inversion attacks can reconstruct sensitive input data from leaked gradients, posing a significant threat to image confidentiality. Meanwhile, traditional image steganography techniques do not take into full consideration the semantic structures inherent in visual content. This can lead to suboptimal embedding locations and limited resilience to semantic perturbations, ultimately resulting in reduced robustness and concealment performance. To address the dual challenge of preserving semantic fidelity and resisting gradient-based inversion attacks in image steganography, this paper proposes an image steganography framework,named CamDWT which integrates semantic attention, frequency-domain embedding, and adversarial reversibility optimization. The proposed method combines Grad-CAM, Discrete Wavelet Transform (DWT), and gradient inversion to achieve semantically aware and robust image steganography. Grad-CAM is used to identify salient regions in the image based on class-specific activations, and secret information is embedded into the high-frequency components of these regions using DWT. During the inversion process, a dual-loss strategy is employed to ensure both gradient consistency and frequency-domain alignment, enhancing the fidelity and recoverability of the hidden content. Experimental results show a high degree of consistency in the salient regions of the original, stego, and reconstructed images. This is validated by four metrics — PCC, cosine similarity, IoU, and Top-K overlap — all meeting the required thresholds. The proposed method achieves an information extraction accuracy of over 98%, representing a 7.3% improvement compared to existing approaches. Moreover, the method exhibits robustness in embedding fidelity and ensures reliable recovery under inversion attacks.

随着边缘协同推理的广泛应用，对数据隐私和模型可解释性的关注日益突出。梯度反转攻击可以利用泄露的梯度重构敏感输入数据，对图像的保密性构成严重威胁。同时，传统的图像隐写技术没有充分考虑视觉内容固有的语义结构。这可能导致次优嵌入位置和对语义扰动的有限弹性，最终导致鲁棒性和隐藏性能降低。为了解决图像隐写中保持语义保真度和抵抗基于梯度的反转攻击的双重挑战，本文提出了一种集成了语义关注、频域嵌入和对抗可逆性优化的图像隐写框架CamDWT。该方法将梯度- cam、离散小波变换（DWT）和梯度反演相结合，实现了语义感知和鲁棒的图像隐写。Grad-CAM基于特定类别的激活来识别图像中的显著区域，并使用DWT将秘密信息嵌入到这些区域的高频成分中。在反演过程中，采用双损耗策略保证梯度一致性和频域对准，增强了隐藏内容的保真度和可恢复性。实验结果表明，在显著区域的原始，隐去和重建图像的高度一致性。这是通过四个指标验证的——PCC、余弦相似性、IoU和Top-K重叠——所有这些指标都满足所需的阈值。该方法的信息提取准确率达到98%以上，与现有方法相比提高了7.3%。此外，该方法在嵌入保真度方面具有鲁棒性，保证了在反攻击下的可靠恢复。

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引用次数: 0

NGCF-RVFL: Next Generation Convolutional Feature with Random Vector Functional Link for multi-grade diabetic retinopathy detection NGCF-RVFL：基于随机向量功能链接的新一代卷积特征用于多级别糖尿病视网膜病变检测

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2026-01-15 DOI: 10.1016/j.compeleceng.2026.110972

Imtiyaz Ahmad, Vibhav Prakash Singh, Manoj Madhava Gore

Diabetic Retinopathy (DR) is one of the leading causes of vision impairment and blindness globally, necessitating early and accurate detection for timely clinical intervention. This paper proposes NGCF-RVFL, a novel Computer-aided-diagnosis system for multi-grade DR detection from retinal fundus images. The working of this system begins with an enhanced preprocessing pipeline that includes median filtering, Gaussian filtering, and Contrast-limited adaptive histogram equalization to reduce noise and improve contrast of the fundus images. Next, we introduce an adaptive image augmentation technique to address the issue of class imbalance. Minority class samples are increased using an augmentation that adapts the size of majority class samples. After that, we propose a Next Generation Convolutional Feature (NGCF) based on the fine-tuned ConvNeXt architecture, consisting of a hierarchical design with four feature extraction stages utilizing depthwise separable convolutions. The NGCF feature effectively encodes intricate retinal structures and disease patterns crucial for accurate DR grading. Further, the discriminative analysis with Principal Component Analysis confirms the significance and effectiveness of the extracted NGC feature in representing relevant retinal information. Furthermore, a lightweight network, Random Vector Functional Link (RVFL), is employed to evaluate the grade-wise detection performance of the proposed NGCF feature. Unlike traditional iterative learning models, the RVFL utilizes a single-pass training mechanism, significantly reducing computation time while maintaining high detection performance. Finally, we evaluate the effectiveness and detection performance of the NGCF feature on other machine learning classifiers such as Support vector machine, Multilayer perceptron, Random forest, and Decision tree. Comprehensive experiments on a benchmark dataset demonstrate that NGCF-RVFL achieves competitive scores across all DR grades with minimal training time, outperforming the state-of-the-art approaches.

糖尿病视网膜病变（DR）是全球视力损害和失明的主要原因之一，需要及早准确发现，及时进行临床干预。本文提出了一种新的基于视网膜眼底图像的多级DR检测计算机辅助诊断系统NGCF-RVFL。该系统的工作从增强的预处理管道开始，包括中值滤波、高斯滤波和对比度有限的自适应直方图均衡化，以减少噪声并提高眼底图像的对比度。接下来，我们引入一种自适应图像增强技术来解决类别不平衡的问题。使用适应多数类样本大小的增强来增加少数类样本。之后，我们提出了基于微调的ConvNeXt架构的下一代卷积特征（NGCF），该架构包括一个分层设计，利用深度可分离卷积进行四个特征提取阶段。NGCF特征有效地编码复杂的视网膜结构和疾病模式，这对准确的DR分级至关重要。此外，主成分分析的判别分析证实了提取的NGC特征在表示相关视网膜信息方面的重要性和有效性。此外，采用了一个轻量级网络随机向量功能链路（RVFL）来评估所提出的NGCF特征的分级检测性能。与传统的迭代学习模型不同，RVFL采用单次训练机制，在保持高检测性能的同时显著减少了计算时间。最后，我们评估了NGCF特征在其他机器学习分类器（如支持向量机、多层感知器、随机森林和决策树）上的有效性和检测性能。在一个基准数据集上进行的综合实验表明，NGCF-RVFL以最少的训练时间在所有DR等级中获得了具有竞争力的分数，优于最先进的方法。

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引用次数: 0