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检索条件"任意字段=IEEE Symposium on Computational Intelligence for Image Processing"

共 8517 条记录，以下是131-140 订阅

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RETINOSCAN: Retinal image-Based Diabetic Retinopathy Detection 3

RETINOSCAN: Retinal Image-Based Diabetic Retinopathy Detecti...

引用

3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Krishna, Nanditha Ashrita Dikshit, V.A. Kavana, G.R. Nishita, S. Rakshit Preran, B. Center for Medical Electronics and Computational Intelligence Dayananda Sagar College of Engineering Department of Medical Electronics Engineering Bengaluru India Dayananda Sagar College of Engineering Department of Medical Electronics Engineering Bengaluru India

ISBN: (纸本)9798331508456

Diabetic Retinopathy (DR) is a leading cause of vision impairment worldwide, necessitating early detection and accurate diagnosis. This study presents a reliable system that leverages advanced image processing and deep learning techniques to identify and classify DR stages. MATLAB is utilized for fundus image preprocessing, integrating a pre-trained Inception-v3 model for feature extraction and stage-wise classification. A web-based graphical user interface enhances clinical applicability. Deployment through MATLAB Compiler and Docker ensures real-world accessibility. The model, trained and validated on a comprehensive retinal image dataset, demonstrates high reliability and accuracy. This system has the potential to improve patient outcomes and establish new standards in automated retinal disease diagnostics. © 2025 ieee.

关键词： Graphical user interfaces

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Assessing Personalized AI Mentoring with Large Language Models in the Computing Field

Assessing Personalized AI Mentoring with Large Language Mode...

引用

computational intelligence in Natural Language processing and Social Media (CI-NLPSoMe Companion), ieee symposium on

作者： Xiao Luo Sean O'Connell Shamima Mithun Department of Management Science and Information Systems Oklahoma State University USA Department of Computer Information Technology Purdue University Indianapolis USA

ISBN: (数字)9798331519742

ISBN: (纸本)9798331519759

This paper provides an in-depth evaluation of three state-of-the-art Large Language Models (LLMs) for personalized career mentoring in the computing field, using three distinct student profiles that consider gender, race, and professional levels. We evaluated the performance of GPT-4, LLaMA 3, and Palm 2 using a zero-shot learning approach without human intervention. A quantitative evaluation was conducted through a custom natural language processing analytics pipeline to highlight the uniqueness of the responses and to identify words reflecting each student's profile, including race, gender, or professional level. The analysis of frequently used words in the responses indicates that GPT-4 offers more personalized mentoring compared to the other two LLMs. Additionally, a qualitative evaluation was performed to see if human experts reached similar conclusions. The analysis of survey responses shows that GPT-4 outperformed the other two LLMs in delivering more accurate and useful mentoring while addressing specific challenges with encouragement languages. Our work establishes a foundation for developing personalized mentoring tools based on LLMs, incorporating human mentors in the process to deliver a more impactful and tailored mentoring experience.

关键词： Surveys Accuracy Social networking (online) Engineering profession Large language models Zero shot learning Pipelines Natural language processing Mentoring computational intelligence

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ieee SSCI 2025 Welcome Message

2025 IEEE Symposium on Computational Intelligence in Natural...

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2025 ieee symposium on computational intelligence in Natural Language processing and Social Media, CI-NLPSoMe 2025 2025年

作者： Haddow, Pauline C. Kayacan, Erdal Garibaldi, Jonathan Norwegian University of Science and Technology Norway Paderborn University Germany University of Nottingham Ningbo China

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A Comparative Study of VGG16, MobileNet, and Ensemble Methods on Fundus image-Based Cataract Detection

A Comparative Study of VGG16, MobileNet, and Ensemble Method...

引用

2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Syahda, Tabina Shafa Nabila Ma'ady, Mochamad Nizar Palefi Lidiawaty, Berlian Rahmy Telkom University Surabaya Department of Information Systems Surabaya Indonesia

ISBN: (纸本)9798331513320

Artificial intelligence (AI) in ophthalmology has been growing, driven by the increasing volume of clinical data that can be utilized for algorithm development. image processing on fundus disease is crucial in providing visual information for cataract detection. Therefore, the application of technology that can leverage fundus images for cataract detection is still a few. In this regard, several previous studies have explored CNN algorithms for automatic cataract screening using fundus images. However, other research groups have proposed or developed new algorithms as alternatives. This research compares two CNN models, VGG16 and MobileNet, while also incorporating additional approaches such as Ensemble Learning and Hybrid Method. VGG16 and MobileNet were chosen because they are known for their high accuracy performance and computational efficiency. Source data obtained from Kaggle as many as 1000 datasets. The results of the study indicate that the model with the Ensemble Learning approach achieved the highest average accuracy, followed by MobileNet, MobileNet + SVM, VGG16, and VGG16 + SVM, with accuracies of 0.9377;0.9150;0.9143;0.9129;and 0.9104, respectively. This research is expected to aid medical professionals in diagnosing cataracts more accurately and guide engineers in selecting the best machine-learning models for fundus image-based cataract detection systems. © 2025 ieee.

关键词： Contrastive Learning

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ieee SSCI 2025 Welcome Message

2025 IEEE Symposium on Computational Intelligence in Natural...

引用

2025 ieee symposium on computational intelligence in Natural Language processing and Social Media, CI-NLPSoMe Companion 2025 2025年

作者： Haddow, Pauline C Kayacan, Erdal Garibaldi, Jonathan Norwegian University of Science and Technology Norway Paderborn University Germany University of No6ngham Ningbo China

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Training Deep Neural Networks with Multi-Objective Adam Optimizer for Medical image Classification

Training Deep Neural Networks with Multi-Objective Adam Opti...

引用

computational intelligence in Health and Medicine (CIHM), ieee symposium on

作者： Farzaneh Nikbakhtsarvestani Shahryar Rahnamayan Mehran Ebrahimi Faculty of Science Ontario Tech University Oshawa Ontario Canada Engineering Department Brock University St. Catharines Ontario Canada

ISBN: (数字)9798331508333

ISBN: (纸本)9798331508340

The multi-objective Adam (MAdam) is a populationbased optimizer that integrates Adam exploitation with evolutionary algorithm exploration. The capability of the MAdam optimizer and its opposition-based extension (OMAdam) to provide a wide range of solutions was previously examined in training neural networks for binary classification. This study investigates the practical applications of these two optimizers in medical image classification, developing two novel diagnostic techniques based on the MNIST and real medical image datasets MedMNIST. For both optimizers, their functionality was demonstrated by generating a set of Pareto fronts comprised of corresponding model parameters in the decision space. In the first approach, false positives and false negatives in binary cross entropy were assigned as two conflicting loss functions. The optimized weight configuration of the corresponding singleobjective Adam optimizer was captured by the set of solutions along the spectrum of the Pareto fronts. MAdam and OMAdam optimizers achieve F1-scores comparable to those of the singleobjective Adam optimizer. In the second approach, by aligning conflicting performance metrics as differentiable loss functions in MAdam, the obtained Pareto front is a direct representation of the performance metrics space, including precision, sensitivity, and specificity.

关键词： Training Pneumonia Evolutionary computation Sensitivity and specificity Breast Entropy Trajectory Medical diagnostic imaging Optimization image classification

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On Frequency Domain Adversarial Vulnerabilities of Volumetric Medical image Segmentation

On Frequency Domain Adversarial Vulnerabilities of Volumetri...

引用

ieee International symposium on Biomedical Imaging

作者： Asif Hanif Muzammal Naseer Salman Khan Fahad Shahbaz Khan Mohamed Bin Zayed University of Artificial Intelligence Linköping University

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

In safety-critical domains like healthcare, resilience of deep learning models towards adversarial attacks is crucial. Volumetric medical image segmentation is a fundamental task, providing critical insights for diagnosis. This paper introduces a novel frequency domain adversarial attack targeting 3D medical data, revealing vulnerabilities in segmentation models. By manipulating the frequency spectrum (low, middle, and high bands), we assess its impact on model performance. Unlike pixel-based 2D attacks, our method continuously perturbs 3D samples with minimal information loss, achieving high fooling rates at lower computational costs and superior black-box transferability, while maintaining perceptual quality. Our code is publicly available at Github † † https://***/asif-hanif/saa.

关键词： image segmentation Solid modeling Three-dimensional displays Tensors computational modeling Frequency-domain analysis Data models computational efficiency Medical diagnostic imaging Spectral analysis

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Algorithmic Bias Detection: A Focus on Skin Tone and Gender Fairness in AI Models

Algorithmic Bias Detection: A Focus on Skin Tone and Gender ...

引用

Trustworthy, Explainable and Responsible computational intelligence (CITREx), ieee symposium on

作者： Happy Iguare Peter Taiwo Center for Equitable AI and Machine Learning Systems Morgan State University Baltimore Maryland USA

ISBN: (数字)9798331520151

ISBN: (纸本)9798331520168

This study delves into the detection of algorithmic bias and fairness benchmarking in Artificial intelligence (AI) and Machine Learning (ML) systems, particularly within Computer Vision applications, with an emphasis on how skin tone variations affect model performance. We analyze gender prediction among leading AI services such as Amazon Rekognition, Deep Face, Fair Face, and the BadgerCake gender-classifier by applying fairness metrics and conducting bias-focused studies across various skin tones. A novel aspect of our methodology involves mapping detected facial attributes and skin tones using a predefined scale, enabling a detailed performance analysis across gender segments. image preprocessing techniques, including contrast enhancement, normalization, and speckle removal, are applied to improve image quality and enhance the reliability of skin tone detection. Using metrics such as equalized odds, calibration, demographic parity, and accuracy parity, our comparative analysis reveals significant disparities, including high accuracy for AWS Rekognition and pronounced biases in Fair Face and BadgerCake for darker skin tones. These findings highlight the need for data augmentation, algorithmic adjustments, and diverse training datasets to mitigate biases and refine algorithms. This research underscores the critical need for continuous evaluation and improvement of AI systems to achieve fair and accurate outcomes across diverse populations.

关键词： Training Measurement Computer vision Accuracy Skin Classification algorithms Calibration Reliability Artificial intelligence Faces

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Spk2ImgMamba: Spiking Camera image Reconstruction with Multi-Scale State Space Models

Spk2ImgMamba: Spiking Camera Image Reconstruction with Multi...

引用

2025 ieee/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Yin, Jiaoyang Fan, Bin Xu, Chao Huang, Tiejun Shi, Boxin School of Computer Science Peking University State Key Lab of Multimedia Info. Processing China School of Computer Science Peking University Nat'l Eng. Research Ctr. of Visual Technology China School of Intelligence Science and Technology Peking University Nat'l Key Lab of General Ai China

ISBN: (纸本)9798331510831

As a bio-inspired vision sensor, the spiking camera has showcased remarkable capability in high-speed imaging with a sampling rate of 40,000 Hz. Reconstructing clear images from continuous spike streams, which is obtained by each photosensor continuously detecting photons and firing them asynchronously, has garnered significant attention. Despite promising results, existing spike-to-image reconstruction methods face challenges in balancing global receptive fields and efficient computation due to the inherent limitations of their backbones. Recently, due to powerful long-range modeling and linear complexity, the state space model (SSM) has emerged as a competitive alternative to CNNs and Transformers. In this paper, we propose a lightweight spike-to-image reconstruction network that harnesses Mamba as the backbone. Our approach sequentially executes three core modules: temporal information integration, spatial feature enhancement, and progressive image reconstruction. The former accumulates cues across diverse temporal windows to explore both long-term and short-term contexts. Subsequently, to model global dependencies while heightening local detail perception, we develop a multi-scale SSM block characterized by multi-scale multi-direction scanning, which effectively boosts spatial feature representations. Finally, intensity images are decoded progressively from the enhanced light-intensity features. Extensive experiments on both synthetic and real-captured data demonstrate that our approach achieves state-of-the-art performance, with only 10% of the network parameters and nearly two orders of magnitude less computational effort. The code will be available at https://***/interstellarH/Spk2ImgMamba. © 2025 ieee.

关键词： Bioimaging

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A Novel Graph-Based Framework for Understanding of Decision-Making Process in Deep Learning Models

A Novel Graph-Based Framework for Understanding of Decision-...

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Trustworthy, Explainable and Responsible computational intelligence (CITREx Companion), ieee symposium on

作者： Basim Azam Pubudu Sanjeewani Brijesh Verma Ashfaqur Rahman Lipo Wang School of Engineering and IT Griffith University Australia University of Melbourne Australia CSIRO Hobart Australia School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9798331519728

ISBN: (纸本)9798331519735

In the realm of deep learning, the lack of transparency in complex models is a major obstacle for wider adoption and trust in artificial intelligence systems. Current explainability methods often provide either localized or global insights into model decisions, but rarely offer a unified perspective that combines both. To address this gap, we propose a novel framework that integrates multiple interpretability techniques, GradCAM and Integrated Gradients to offer a comprehensive, multi-perspective explanation of deep learning models in terms of graphs. Our framework initially identifies key regions of interest in the input data offering better understanding of model behaviour. The salient regions are then mapped into a graph-based representation, where nodes represent clusters of important features, and edges represent the intensity of their interactions. This dual-layer approach provides both intuitive visual heatmaps for localized insights and graph structures for understanding feature interactions. The aim of this work is to improve model transparency and interpretability by combining visual and structural explanations, thus enabling a more comprehensive understanding of complex deep learning models. To validate the proposed framework, we conduct experiments using pre-trained networks on image classification dataset. Our results demonstrate that the proposed explainability framework not only enhances interpretability by highlighting critical regions but also offers novel insights into the relationships between features through graph structures. Our code is available at https://***/pubudusanjeewani/arcdp-2021-2024/tree/main/ssci2025

关键词： Deep learning Heating systems Visualization Codes computational modeling Decision making Data models Convolutional neural networks computational intelligence image classification

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