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CheX-DS: Improving Chest X-ray Image Classification with Ensemble Learning Based on DenseNet and Swin Transformer

CheX-DS: Improving Chest X-ray Image Classification with Ens...

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2024 ieee International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Xinran Xu, Xiujuan Liu, Yu Zhao, Xiaowei Dalian University of Technology School of Software Technology Dalian China

ISBN: (纸本)9798350386226

The automatic diagnosis of chest diseases is a popular and challenging task. Most current methods are based on convolutional neural networks (CNNs), which focus on local features while neglecting global features. Recently, self-attention mechanisms have been introduced into the field of computer vision, demonstrating superior performance. Therefore, this paper proposes an effective model, CheX-DS, for classifying long-tail multi-label data in the medical field of chest X-rays. The model is based on the excellent CNN model DenseNet for medical imaging and the newly popular Swin Transformer model, utilizing ensemble deep learning techniques to combine the two models and leverage the advantages of both CNNs and Transformers. The loss function of CheX-DS combines weighted binary cross-entropy loss with asymmetric loss, effectively addressing the issue of data imbalance. The NIH ChestX-ray14 dataset is selected to evaluate the model's effectiveness. The model outperforms previous studies with an excellent average AUC score of 83.76%, demonstrating its superior performance. © 2024 ieee.

关键词： Convolutional neural networks

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Predictive Modeling and Decision-Making in Data Science: A Comparative Study 5

Predictive Modeling and Decision-Making in Data Science: A C...

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5th ieee International conference on Recent Trends in computer Science and Technology, ICRTCST 2024

作者： Valli, Latha Narayanan Sujatha, N. Standard Chartered Global Business Services Sdn Bhd. Kuala Lumpur Malaysia PG and Research Department of Computer Science Madurai India

ISBN: (纸本)9798350351378

The study presents a comprehensive investigation into the intricate relationship between predictive modelling techniques, model interpretability, and ethical considerations in data-driven decision-making. The research design integrates quantitative and qualitative analyses. Model performances are assessed using accuracy, precision, recall, F1-Score, and AUC-ROC metrics on distinct datasets. Furthermore, techniques such as local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) are employed to enhance model interpretability. Ethical and social implications are examined through bias assessment, privacy evaluation, and transparency measures. The outcomes highlight the variability in model performance across datasets and decision-making scenarios. Neural networks consistently exhibit competitive accuracy and F1-Scores. The interpretability techniques illuminate feature contributions, allowing stakeholders to comprehend complex model decisions. Ethical analysis reveals the presence of minor gender bias and showcases the efficacy of privacy-preserving methods. This study serves as a comprehensive guide for practitioners, policymakers, and organisations seeking to harness the power of predictive modelling in decision-making. © 2024 ieee.

关键词： Decision making

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WSNet: Towards An Effective Method for Wound Image Segmentation 23

WSNet: Towards An Effective Method for Wound Image Segmentat...

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23rd ieee/CVF Winter conference on Applications of computer Vision (WACV)

作者： Oota, Subba Reddy Rowtula, Vijay Mohammed, Shahid Liu, Minghsun Gupta, Manish Woundtech Innovat Healthcare Solut Hyderabad Telangana India IIIT Hyderabad Hyderabad Telangana India Microsoft Redmond WA USA

ISBN: (纸本)9781665493468

Medical image segmentation is critical for effective computer-aided diagnosis and localization of ailments. Automated segmentation of wound regions from patient images can aid clinicians in measuring and managing chronic wounds and monitoring the wound healing trajectory. While there exists a plethora of work on general medical image segmentation, there is hardly any work on wound image analysis and segmentation. Existing methods are limited to segmenting a smaller subset of ulcers, such as foot ulcers, with no special processing for wound images. In this paper, we build segmentation models for eight different types of wound images. Wound image analysis is a challenging problem due to the lack of availability of extensive data (labeled or unlabeled), and annotation is also challenging due to the shortage of well-trained wound care clinicians. To handle these challenges, we contribute WOUNDSEG(1), a large and diverse dataset of segmented wound images. Generic wound image segmentation is complex due to the heterogeneous appearance of wound area across images of similar wound types. We propose a novel image segmentation framework, WSNET, which leverages (a) wound-domain adaptive pretraining on a large unlabeled wound image collection and (b) a global-local architecture that utilizes full image and its patches to learn fine-grained details of heterogeneous wounds. On WOUNDSEG, we achieve a decent Dice score of 0.847. On existing AZH Woundcare and Medetec datasets, we establish a new state-of-the-art. Further, we show the impact of using segmentation for improving the accuracy of downstream tasks like wound area and volume prediction.

关键词： Image segmentation Wireless sensor networks Adaptation models Image analysis computer architecture Wounds Trajectory

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Optimal Link Configuration for Covert Heterogeneous Wireless networks with Power Constraints 59

Optimal Link Configuration for Covert Heterogeneous Wireless...

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59th Annual conference on Information Sciences and Systems, CISS 2025

作者： Gillani, Amna Lorenzo, Beatriz Ghaderi, Majid Dagefu, Fikadu Goeckel, Dennis University of Massachusetts Amherst Department of Electrical and Computer Engineering AmherstMA01003 United States University of Calgary Department of Computer Science CalgaryABT2N 1N4 Canada AdelphiMD20783 United States

ISBN: (纸本)9798331513269

Heterogeneous networks (HetNets) leverage multiple communication interfaces (e.g., Wi-Fi, cellular, Bluetooth) seamlessly integrated to enhance connectivity and capacity across diverse environments. These capabilities can be advantageous for covert communication, where the objective is to ensure undetectable transmission in the presence of an adversary. This work investigates optimal power allocation strategies for covert communication within HetNets under maximum power constraints. We propose a polynomial-time (m2) algorithm for optimal link configuration by allocating power across multiple modes. Next, we address network-level covertness by allocating the end-to-end covertness constraint across links in a two-hop system that maximizes throughput. Leveraging the link configuration approach, we derive the optimal power allocation for each mode and link, ensuring the overall transmission remains undetectable. Numerical results demonstrate that the proposed method outperforms existing single-link and two-hop approaches, achieving higher efficiency and security in covert communication across HetNets. © 2025 ieee.

关键词： Wireless local area networks (WLAN)

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Massive Dimensions Reduction and Hybridization with Meta-heuristics in Deep Learning

Massive Dimensions Reduction and Hybridization with Meta-heu...

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ieee Canadian conference on Electrical and computer Engineering (CCECE)

作者： Khosrowshahli, Rasa Rahnamayan, Shahryar Ombuki-Berman, Beatrice Brock Univ Nat Inspired Computat Intelligence NICI Lab St Catharines ON Canada Brock Univ Bioinspired Computat Intelligence Res Grp BICIG St Catharines ON Canada Brock Univ Dept Comp Sci St Catharines ON Canada Brock Univ Dept Engn St Catharines ON Canada

ISBN: (纸本)9798350371635;9798350371628

Deep learning is mainly based on utilizing gradient-based optimization for training Deep Neural Network (DNN) models. Although robust and widely used, gradient-based optimization algorithms are prone to getting stuck in local minima. In this modern deep learning era, the SOTA DNN models have millions and billions of parameters, including weights and biases, making them huge-scale optimization problems in terms of search space. Tuning a huge number of parameters is a challenging task that causes vanishing/exploding gradients and overfitting;likewise, utilized loss functions do not exactly represent our targeted performance metrics. A practical solution to exploring large and complex solution space is meta-heuristic algorithms. Since DNNs exceed thousands and millions of parameters, even robust meta-heuristic algorithms, such as Differential Evolution, struggle to efficiently explore and converge in such huge-dimensional search spaces, leading to very slow convergence and high memory demand. To tackle the mentioned curse of dimensionality, the concept of blocking was recently proposed as a technique that reduces the search space dimensions by grouping them into blocks. In this study, we aim to introduce Histogram-based Blocking Differential Evolution (HBDE), a novel approach that hybridizes gradient-based and gradient-free algorithms to optimize parameters. Experimental results demonstrated that the HBDE could reduce the parameters in the ResNet-18 model from 11M to 3K during the training/optimizing phase by meta-heuristics, namely, the proposed HBDE, which outperforms baseline gradient-based and parent gradient-free DE algorithms evaluated on CIFAR10 and CIFAR100 datasets showcasing its effectiveness with reduced computational demands for the very first time.

关键词： Deep Neural networks Gradient-free Optimization Hybridization Huge-scale Optimization Differential Evolution Dimension Reduction Block Population-based Algorithms

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Federated Multi-task Learning in Distributed networks

Federated Multi-task Learning in Distributed Networks

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2024 ieee International conference on Big Data, BigData 2024

作者： Hong, Lingzhou Garcia, Alfredo Texas A&m University Department of Industrial & Systems Engineering College Station United States

ISBN: (纸本)9798350362480

We consider a Distributed Federated multitask learning scheme that accounts for multiple linear model estimation tasks with heterogeneous and/or correlated data streams. We assume that nodes can be partitioned into groups corresponding to different learning tasks and communicate according to a directed network topology. Each node estimates a linear model asynchronously and is subject to local (within-group) regularization and global (across groups) regularization terms targeting noise reduction and generalization performance improvement, respectively. We provide a finite-time characterization of the convergence of the estimators and task relation and illustrate the scheme's general applicability of random field temperature estimation. © 2024 ieee.

关键词： Federated learning

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GLSI Texture Descriptor Based on Complex networks for Music Genre Classification

GLSI Texture Descriptor Based on Complex Networks for Music ...

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International Joint conference on Neural networks (IJCNN)

作者： Coca Salazar, Andres Eduardo Fed Univ Technol Parana UTFPR TD Comp Engn Coordinat COENC Toledo PR Brazil

ISBN: (纸本)9781665488679

The texture classification of an image is related to an important musical attribute, the music genre. This relationship is depicted in the visual representation of the audio signal, called as spectrogram. In this paper, we propose a new Music Genre Classification (MGC) system that processes the spectrogram texture using the Gray Level and Structural Information (GLSI) descriptor, and represents the interconnection between the descriptor codes through complex networks. The GLSI descriptor is an improvement of the CLBP (Completed local Binary Pattern) descriptor, which quantifies the texture of an image with three codes: signal (CLBP-S), magnitude (CLBP-M), and central (CLBP-C). By transforming the CLBP-C code, GLSI adds macro-structural information. The network nodes represent the descriptor codes, and the respective edges, the relationship according to the horizontal and vertical consecutive condition. We defined two representations for the nodes: 1) individual code node, obtaining the G(s), G(m) and G(g) networks, and 2) triple code node, obtaining the G(smg) network. For the experimental stage, we used the GTZAN dataset, three types of spectrograms: conventional, mel-spectrogram and gammatonegram;and mining with network topological measures. For each type of spectrogram, we performed three experiments according to feature vector combinations, such as measures of: 1) G(s), G(m) and G(g), 2) G(smg), and 3) all networks. In the machine learning stage, we used the ensemble classifier Bagging with Random Forest, and 10fold cross-validation repeated 100 times. The experiment using all measures and all spectrograms revealed a satisfactory result, indicating that the MGC proposed is promising. We also propose a new equation to calculate the GLSI code, which proved to be much faster and with more intuitive encoding.

关键词： Digital image processing computer vision applications pattern recognition performance evaluation

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Fast and Accurate Community Detection in Social Media networks with a Multi-Algorithmic Approach

Fast and Accurate Community Detection in Social Media Networ...

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2024 International conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Pabitha, P. Petchiammal, G. Eswari Anna University Department of Computer Technology MIT Campu Chromepet600044 India Knowledge Institute of Technology Department of Computer Science and Engineering Salem637504 India

ISBN: (纸本)9798331543617

Community detection is an essential technique for uncovering structural patterns in complex networks, such as social, biological, and information networks. In this paper to explore a hybrid approach to community detection by combining the label propagation algorithm and leiden algorithm. Although LPA is well known for its computational effectiveness in large- scale networks, because of its reliance on local label propagation, it frequently results in unstable and fractured communities. High- quality community structures are produced by the Leiden algorithm, which is more computationally expensive than the Louvain method but ensures well-connected communities by optimising modularity. Our goal in combining these two approaches is to take use of LPA's speed for quick community discovery and the Leiden algorithm's refinement powers for increased accuracy and stability. Large or dynamic networks where both real-time speed and community quality are crucial are best suited for the suggested hybrid method. The combination of these algorithms yields more cohesive and modular communities than either one working alone, as evidenced by experimental results on synthetic social network data. The method is a useful tool for large- scale network analysis because it strikes a balance between the necessity for strong and significant community structures and computational efficiency. © 2024 ieee.

关键词： Community detection Label propagation leidens Louvain

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FCMDNet:A Feature Correlation-Based Multi-Scale Dual-Branch Network for Infrared and Visible Image Fusion 6

FCMDNet:A Feature Correlation-Based Multi-Scale Dual-Branch ...

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6th International conference on Robotics and computer Vision, ICRCV 2024

作者： Jiang, Zheng Chen, Xiangyang Zan, Guo Yang, Mingyu School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331527426

In recent years, deep learning techniques have been widely used in the field of infrared and visible image fusion (IVIF) due to their powerful feature representation. Existing methods usually use convolutional neural network (CNN), Transformer or Transformer-CNN to process different modal images. However, if the relationship between modality-specific and modality-shared features is ignored in the fusion network, the fused image can result in disturbed or missing modality information. To solve this problem, we propose a multi-scale dual-branch fusion network based on Transformer and CNN, called FCMDNet. Firstly, FCMDNet network introduces Multi-scale Patch Embedding based on Restormer module to extract multi-scale shallow features across modalities. Then, we design a dual-branch Transformer-CNN feature extractor, where the Transformer branch exploits the long and short-range attention of the Lite Transformer for low-frequency global features, the CNN branch uses residual dense connections to extract high-frequency local features. In addition, we propose a loss function based on feature correlation that exploits the correlation of low-frequency features and the uncorrelation of high-frequency features between different modalities to improve the fusion quality. Finally, an addition fusion strategy and two fusion layers are used to fuse the global and local features to obtain the final fused image. Experimental results on two public datasets show that our proposed FCMDNet has good performance on all of them. © 2024 ieee.

关键词： Convolutional neural networks

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Ranking the Spreading Influence of Nodes in Complex networks by Combining local Average Weight and Average Normalized Link Entropy 4

Ranking the Spreading Influence of Nodes in Complex Networks...

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4th International conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2023

作者： Fan, Kaiyu Yang, Fan Tu, Dexin Tang, Xinlai Mao, Xiuyong Guangxi University of Science and Technology School of Computer Science and Technology Liuzhou545006 China Guangxi University of Science and Technology Key Laboratory of Intelligent Information Processing and Graph Processing Liuzhou545006 China

ISBN: (纸本)9798350343618

Ranking the spreading influence of nodes is of great importance for controlling the spreading process on networks. Although a few measures are proposed for ranking nodes in weighted networks, it is still a challenge in accurately and efficiently ranking the spreading influence of nodes in weighted networks. In this paper, by utilizing both of the local and global topological structure information, a novel method is proposed. First, we define a local average weight (LAW) to describe the local topological structure information of nodes. Second, partly based on the link diversity, we define an average normalized link entropy (ANLE) to describe the global topological structure information of nodes. Further, by combining the LAW and ANLE, we propose a novel ranking measure termed as LAW-ANLE (LA) centrality. The proposed LA is verified by extensive experiments on a series of networks, and the results show that the LA is feasible and effective in accurately ranking the spreading influence of nodes. © 2023 ieee.

关键词： Complex networks

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