A Deep Ensemble Dynamic Learning Network for Corona Virus Disease 2019 Diagnosis

Zhang, Zhijun; Chen, Bozhao; Luo, Yu

doi:10.1109/tnnls.2022.3201198

Cited by 7 publications

(6 citation statements)

References 42 publications

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“…Generally, the classical machine learning based methods require two stages for the classification task. In the first stage, multiple features of medical images are extracted, and then the features are fed to classifiers, such as support vector machine (SVM), Markov random fields, random forests (RF), neural networks, in the second stage for classification [15]- [19].…”

Section: A Machine Learning Techniques For Medical Image Classificationmentioning

confidence: 99%

“…It is an infection caused by viruses, bacteria or fungi, which will result in inflammation in the lungs and adversely affect the alveoli. Especially in the past year, Coronavirus disease 2019 (COVID- 19), caused by a novel corona-virus (SARS-CoV-2), has become the most severe epidemic disease in the world. So far, 215 countries and territories have been affected by the COVID-19 epidemic.…”

Section: Introductionmentioning

confidence: 99%

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A Distance Transformation Deep Forest Framework With Hybrid-Feature Fusion for CXR Image Classification

Hong

Lin

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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Detecting pneumonia, especially COVID-19, from chest X-ray (CXR) images is one of the most effective ways for disease diagnosis and patient triage. The application of deep neural network for CXR image classification is limited due to the small sample size of the well-curated data. To tackle this problem, this paper proposes a distance transformation-based deep forest framework with the hybrid-feature fusion (DTDF-HFF) for accurate CXR image classification. In our proposed method, hybrid features of CXR images are extracted by two ways: handcrafted feature extraction and multi-grained scanning. Different types of features are fed into different classifiers in the same layer of the deep forest, and the prediction vector obtained at each layer is transformed to form distance vector based on a self-adaptive scheme. The distance vectors obtained by different classifiers are fused and concatenated with the original features, then input into the corresponding classifier at the next layer. The cascade grows until DTDF-HFF can no longer gain the benefits from the new layer. We compare the proposed method with other methods on the public CXR data sets, and the experimental results show that the proposed method can achieve state-of-the art performance. The code will be made publicly available at https://github.com/hongqq/DTDF-HFF.

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Section: A Machine Learning Techniques For Medical Image Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Distance Transformation Deep Forest Framework With Hybrid-Feature Fusion for CXR Image Classification

Hong

Lin

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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“…By adopting ZeaD formulas ( 6)- (10) to discretize CTQRD model (5), respectively, the following DTQRD-1, DTQRD-2, DTQRD-3, DTQRD-4, and DTQRD-5 models are derived and acquired:…”

Section: Discrete-time Modelsmentioning

confidence: 99%

“…At present, the research on more challenging time-varying problems has become a new hotspot, and many new methods have been proposed and applied [1][2][3][4][5]. As a neural dynamics method with neural network background, the zeroing neural dynamics (ZND) method is proposed and applied to solve different kinds of time-varying problems [6][7][8][9][10][11][12][13][14][15][16], such as time-varying linear matrix inequality [6], robot control [9], corona virus disease diagnosis [10], matrix inversion [13,14], and timevarying nonlinear optimization [16]. Generally, the problem solving model obtained by using the ZND method is a continuous one.…”

Section: Introductionmentioning

confidence: 99%

Continuous and Discrete ZND Models with Aid of Eleven Instants for Complex QR Decomposition of Time-Varying Matrices

Chen,

Kang,

Zhang

2023

Mathematics

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The problem of QR decomposition is considered one of the fundamental problems commonly encountered in both scientific research and engineering applications. In this paper, the QR decomposition for complex-valued time-varying matrices is analyzed and investigated. Specifically, by applying the zeroing neural dynamics (ZND) method, dimensional reduction method, equivalent transformations, Kronecker product, and vectorization techniques, a new continuous-time QR decomposition (CTQRD) model is derived and presented. Then, a novel eleven-instant Zhang et al discretization (ZeaD) formula, with fifth-order precision, is proposed and studied. Additionally, five discrete-time QR decomposition (DTQRD) models are further obtained by using the eleven-instant and other ZeaD formulas. Theoretical analysis and numerical experimental results confirmed the correctness and effectiveness of the proposed continuous and discrete ZND models.

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“…The automated interpretation draws the attention of researchers toward video summarization and context-aware systems [8] that can reduce the storage cost along with processing time when video footage is retrieved. The machine learning models are extensively used in object detection [9], emotion recognition [10], disease detection [11], and many more to mention. However, this proposed research is about training a machine on a synthetic dataset for image/video scene classification problems.…”

mentioning

confidence: 99%

Real-Time Event-Driven Road Traffic Monitoring System Using CCTV Video Analytics

Tahir,

Qiao,

Kanwal

et al. 2023

IEEE Access

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Closed-circuit television (CCTV) systems have become pivotal tools in modern urban surveillance and traffic management, contributing significantly to road safety and security. This paper introduces an effective solution that capitalizes on CCTV video analytics and an event-driven framework to provide realtime updates on road traffic events, enhancing road safety. Furthermore, this system minimizes the storage requirements for visual data while retaining crucial details related to road traffic events. To achieve this, a two-step approach is employed: (1) training a Deep Convolutional Neural Network (DCNN) model using synthetic data for the classification of road traffic (accident) events and (2) generating video summaries for the classified events. Privacy laws make it challenging to obtain extensive real-world traffic data from open-source datasets, and this challenge is addressed by creating a customised synthetic visual dataset for training. The evaluation of the synthetically trained DCNN model is conducted on ten real-time videos under varying environmental conditions, yielding an average accuracy of 82.3% for accident classification (ranging from 56.7% to 100%). The test video related to the night scene had the lowest accuracy at 56.7% because there was a lack of synthetic data for night scenes. Furthermore, five experimental videos were summarized through the proposed system, resulting in a notable 23.1% reduction in the duration of the original fulllength videos. Overall, this proposed system holds significant promise for event-based training of intelligent vehicles in Intelligent Transport Systems (ITS), facilitating rapid responses to road traffic incidents and the development of advanced context-aware systems.

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A Deep Ensemble Dynamic Learning Network for Corona Virus Disease 2019 Diagnosis

Cited by 7 publications

References 42 publications

A Distance Transformation Deep Forest Framework With Hybrid-Feature Fusion for CXR Image Classification

A Distance Transformation Deep Forest Framework With Hybrid-Feature Fusion for CXR Image Classification

Continuous and Discrete ZND Models with Aid of Eleven Instants for Complex QR Decomposition of Time-Varying Matrices

Real-Time Event-Driven Road Traffic Monitoring System Using CCTV Video Analytics

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