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Violence detection Using Spatiotemporal Features with 3d convolutional neural network

SENSORS 2019年第11期19卷 2472.-2472.页

作者： Ullah, Fath U. Min Ullah, Amin Muhammad, Khan Ul Haq, Ijaz Baik, Sung Wook Sejong Univ Digital Contents Res Inst Intelligent Media Lab Seoul 143747 South Korea Sejong Univ Dept Software Seoul 143747 South Korea

The worldwide utilization of surveillance cameras in smart cities has enabled researchers to analyze a gigantic volume of data to ensure automatic monitoring. An enhanced security system in smart cities, schools, hospitals, and other surveillance domains is mandatory for the detection of violent or abnormal activities to avoid any casualties which could cause social, economic, and ecological damages. Automatic detection of violence for quick actions is very significant and can efficiently assist the concerned departments. In this paper, we propose a triple-staged end-to-end deep learning violence detection framework. First, persons are detected in the surveillance video stream using a light-weight convolutional neural network (CNN) model to reduce and overcome the voluminous processing of useless frames. Second, a sequence of 16 frames with detected persons is passed to 3d CNN, where the spatiotemporal features of these sequences are extracted and fed to the Softmax classifier. Furthermore, we optimized the 3d CNN model using an open visual inference and neural networks optimization toolkit developed by Intel, which converts the trained model into intermediate representation and adjusts it for optimal execution at the end platform for the final prediction of violent activity. After detection of a violent activity, an alert is transmitted to the nearest police station or security department to take prompt preventive actions. We found that our proposed method outperforms the existing state-of-the-art methods for different benchmark datasets.

关键词： abnormal activity deep learning 3d convolutional neural network violence detection surveillance cameras

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Automatic identification of schizophrenia based on EEG signals using dynamic functional connectivity analysis and 3d convolutional neural network

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COMPUTERS IN BIOLOGY ANd MEdICINE 2023年第1期160卷 107022-107022页

作者： Shen, Mingkan Wen, Peng Song, Bo Li, Yan Univ Southern Queensland Sch Engn Toowoomba Australia Univ Southern Queensland Sch Math Phys & Comp Toowoomba Australia

Schizophrenia (ScZ) is a devastating mental disorder of the human brain that causes a serious impact of emotional inclinations, quality of personal and social life and healthcare systems. In recent years, deep learning methods with connectivity analysis only very recently focused into fMRI data. To explore this kind of research into electroencephalogram (EEG) signal, this paper investigates the identification of ScZ EEG signals using dy-namic functional connectivity analysis and deep learning methods. A time-frequency domain functional con-nectivity analysis through cross mutual information algorithm is proposed to extract the features in alpha band (8-12 Hz) of each subject. A 3d convolutional neural network technique was applied to classify the ScZ subjects and health control (HC) subjects. The LMSU public ScZ EEG dataset is employed to evaluate the proposed method, and a 97.74 +/- 1.15% accuracy, 96.91 +/- 2.76% sensitivity and 98.53 +/- 1.97% specificity results were achieved in this study. In addition, we also found not only the default mode network region but also the con-nectivity between temporal lobe and posterior temporal lobe in both right and left side have significant differ-ence between the ScZ and HC subjects.

关键词： ScZ EEG Cross mutual information 3d convolutional neural network default mode network

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Isotropic Brain MRI Reconstruction from Orthogonal Scans Using 3d convolutional neural network

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SENSORS 2024年第20期24卷 6639页

作者： Tian, Jinsha Xiao, Canjun Zhu, Hongjin Chengdu Technol Univ Sch Big Data & Artificial Intelligence Chengdu 611730 Peoples R China Chengdu Technol Univ Digital Twin Lab Chengdu 611730 Peoples R China

As an alternative to true isotropic 3d imaging, image super-resolution (SR) has been applied to reconstruct an isotropic 3d volume from multiple anisotropic scans. However, traditional SR methods struggle with inadequate performance, prolonged processing times, and the necessity for manual feature extraction. Motivated by the exceptional representational ability and automatic feature extraction of convolutional neural networks (CNNs), in this work, we present an end-to-end isotropic MRI reconstruction strategy based on deep learning. The proposed method is based on 3d convolutional neural networks (3d CNNs), which can effectively capture the 3d structural features of MRI volumes and accurately predict potential structure. In addition, the proposed method takes multiple orthogonal scans as input and thus enables the model to use more complementary information from different dimensions for precise inference. Experimental results show that the proposed algorithm achieves promising performance in terms of both quantitative and qualitative assessments. In addition, it can process a 3d volume with a size of 256 x 256 x 256 in less than 1 min with the support of an NVIdIA GeForce GTX 1080Ti GPU, which suggests that it is not only a quantitatively superior method but also a practical one.

关键词： 3d convolutional neural network isotropic reconstruction magnetic resonance imaging super-resolution orthogonal scans

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MBANet: A 3d convolutional neural network with multi-branch attention for brain tumor segmentation from MRI images

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BIOMEdICAL SIGNAL PROCESSING ANd CONTROL 2023年第Part1期80卷 000-000页

作者： Cao, Yuan Zhou, Weifeng Zang, Min An, dianlong Feng, Yan Yu, Bin Qingdao Univ Sci & Technol Coll Informat Sci & Technol Sch Data Sci Qingdao 266061 Peoples R China Qingdao Univ Sci & Technol Coll Math & Phys Qingdao 266061 Peoples R China Univ Sci & Technol China Sch Data Sci Hefei 230027 Peoples R China

More than half of brain tumors are malignant tumors, so there is a need for fast and accurate segmentation of tumor regions in brain Magnetic Resonance Imaging (MRI) images. Traditional 2d brain tumor segmentation methods seriously ignore the spatial context features of brain tumor MRI images, so how to achieve accurate segmentation of brain tumor regions with multiple modalities is the main problem. The paper proposes a 3d convolutional neural network with 3d multi-branch attention -MBANet. First, the optimized shuffle unit is used to form the basic unit (BU) module of MBANet. In the BU module, the group convolution is used to perform convolution operation after the input channel is split, and the channel shuffle is used to scramble the con-volutional channels after fusion. Then, MBANet uses a novel multi-branch 3d Shuffle Attention (SA) module as the attention layer in the encoder. The 3d SA module groups along the channel dimension and divides the feature maps into small features. For each small feature, the 3d SA module builds both channel attention and spatial attention while adopting the BU module. In addition, in order to recover the resolution of the upsampling se-mantic features better, a 3d SA module is also used in the skip connection of MBANet. Experiments on the BraTS 2018 and BraTS 2019 show that the dice of ET, WT and TC reach 80.18%, 89.80%, 85.47% and 78.21%, 89.79%, 83.04%, respectively. The excellent segmentation performance shows that MBANet is significantly improved compared with other state-of-the-art methods.

关键词： MBANet Brain tumor segmentation 3d multi -branch attention 3d convolutional neural network

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A 3d convolutional neural network for Volumetric Image Semantic Segmentation

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Procedia Manufacturing 2019年 39卷 422-428页

作者： Hongya Lu Haifeng Wang Qianqian Zhang Sang Won Yoon daehan Won

This research proposes a novel 3d convolutional neural network (CNN) to perform organ tissue segmentation from volumetric 3d medical images. Accurate and efficient segmentation on the 3d medical image of human organ is a critical step towards disease diagnosis. For volumetric 3d medical image segmentation tasks, the effectiveness of conventional 2d CNNs are reduced due to loss of spatial information. To overcome the obstacles, a 3d CNN that implements the convolution and pooling processes in a 3d space is applied as a substitution to the patch division scheme of 2d CNNs. By using 3d CNN, the image becomes scalable in the spatial direction, allowing accurate image detection with different frame sizes. The 3d CNN implements a cube-by-cube scanning strategy, followed by 3d transformation for each cube in terms of convolving and pooling. The segmentation results of 3d CNN is tested on a 3d OCT image dataset of human thyroid. Experimental results demonstrate that the 3d CNN approach obtains outstanding consistency and accuracy.

关键词： 3d convolutional neural network volumetric OCT image segmentation

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Vegetation Type Classification Based on 3d convolutional neural network Model: A Case Study of Baishuijiang National Nature Reserve

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FORESTS 2022年第6期13卷 906页

作者： Zhou, Xinyao Zhou, Wenzuo Li, Feng Shao, Zhouling Fu, Xiaoli Southwest Univ Sch Geog Sci Chongqing 400715 Peoples R China

Efficient and accurate vegetation type extraction from remote sensing images can provide decision makers with basic forest cover and land use information, and provides a reliable basis for long-term monitoring. With the development of deep learning, the convolutional neural network (CNN) has been used successfully to classify tree species in many studies, but CNN models have rarely been applied in the classification of vegetation types on larger scales. To evaluate the performance of CNN models in the classification of vegetation types, this paper compared the classification accuracy of nine dominant land cover types in Baishuijiang National Nature Reserve with four models: 3d-CNN, 2d-CNN, JSSAN (joint spatial-spectral attention network) and Resnet18, using sentinel-2A data. Comparing the difference in classification accuracy between the direct use of raw sentinel images and fused feature indices sentinel images, the results showed that adding feature indices can improve the overall accuracy of the model. After fusing the characteristic bands, the accuracy of the four models was improved significantly, by 5.46-19.33%. The best performing 3d-CNN model achieved the highest classification accuracy with an overall accuracy of 95.82% and a kappa coefficient of 95.07%. In comparison, 2d-CNN achieved an overall accuracy of 79.07% and a kappa coefficient of 75.44%, JSSAN achieved an overall accuracy of 81.67% and a kappa coefficient of 78.56%, and Resnet18 achieved an overall accuracy of 93.61% and a kappa coefficient of 92.45%. The results showed that the 3d-CNN model can effectively capture vegetation type cover changes from broad-leaved forests at lower elevation, to shrublands and grasslands at higher elevation, across a range spanning 542-4007 m. In experiments using a small amount of sample data, 3d-CNN can better incorporate spatial-spectral information and is more effective in distinguishing the performance of spectrally similar vegetation types, providing an effici

关键词： vegetation types classification 3d convolutional neural network Baishuijiang National Nature Reserve

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A real-time continuous monitoring system for long-term voltage stability with sliding 3d convolutional neural network

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 134卷 107378-107378页

作者： Cai, Huaxiang Hill, david J. Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China

A real-time continuous monitoring system (CMS) for long-term voltage stability assessment with sliding three-dimensional convolutional neural network (3d-CNN) is proposed in this paper. Given a power system, its dynamic responses and topological information are strategically fused and converted into sequential state images, which constitute the learning input of the sliding 3d-CNN. In this way, both spatial and temporal correlation are considered in the CMS. The bus indices are reordered to strengthen the topology information in these images. With the localized weight-shared convolution operations, 3d-CNN can be conducted more efficiently with fewer parameters, and extract features independently of spatial locations. The translation invariance characteristic makes 3d-CNN highly generic to unknown scenarios, which is further enhanced after bus indices reordering. The sliding window of the 3d-CNN enables the CMS to handle varying degrees of disturbance and performs continuous online monitoring. Numerical tests are carried out on the New England 10-generator-39-bus system to demonstrate the effectiveness. Test results also show the potential of the CMS in robustness to PMU measurement errors and information losses.

关键词： Voltage stability assessment Bus indices 3d convolutional neural network Spatial-temporal correlations

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4CF-Net: New 3d convolutional neural network for spectral spatial classification of hyperspectral remote sensing images

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING ANd ARCHITECTURE OF GAZI UNIVERSITY 2022年第1期37卷 439-453页

作者： Huseyin, Firat Hanbay, davut Dicle Univ Vocat Sch Tech Sci TR-21200 Diyarbakir Turkey Inonu Univ Dept Comp Engn TR-44280 Malatya Turkey

Hyperspectral images (HSI) are contiguous band images commonly used in remote sensing. deep learning (dL) is an effective method used in HSI classification. convolutional neural networks (CNN) are one of the dL methods used in HSI classification. It provides automated approaches that can learn abstract features of HSIs from spectral-spatial fields. The high dimensionality of the HSIs increases the computational complexity. Therefore, most of the developed CNN models perform dimensionality reduction as a preprocessing step. Another problem in HSI classification is that spectral-spatial features must be considered in order to obtain accurate results. Because, HSI classification performance is highly dependent on spectral spatial information. In this study, a new 3d CNN model is proposed for HSI classification. The proposed method provides an effective method to simultaneously extract spectral-spatial features in HSIs. The network uses the 3d hyperspectral cube at the input. Principal component analysis is used to eliminate the dimensional redundancy in the hyperspectral cube. Then, using neighborhood extraction, spectral-spatial features are extracted effectively. The proposed method has been tested with 4 datasets. The application results were compared with 7 different dL-based methods and it was seen that our 4CF-Net method showed better classification performance.

关键词： Hyperspectral image classification deep learning 3d convolutional neural network remote sensing

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A Spectral-Spatial Cascaded 3d convolutional neural network with a convolutional Long Short-Term Memory network for Hyperspectral Image Classification

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REMOTE SENSING 2019年第20期11卷 2363-2363页

作者： Qi, Wenchao Zhang, Xia Wang, Nan Zhang, Mao Cen, Yi Chinese Acad Sci Inst Remote Sensing & Digital Earth State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Univ Chinese Acad Sci Beijing 100101 Peoples R China

deep learning methods used for hyperspectral image (HSI) classification often achieve greater accuracy than traditional algorithms but require large numbers of training epochs. To simplify model structures and reduce their training epochs, an end-to-end deep learning framework incorporating a spectral-spatial cascaded 3d convolutional neural network (CNN) with a convolutional long short-term memory (CLSTM) network, called SSCC, is proposed herein for HSI classification. The SSCC framework employs cascaded 3d CNN to learn the spectral-spatial features of HSIs and uses the CLSTM network to extract sequence features. Residual connections are used in SSCC to accelerate model convergence, with the outputs of previous convolutional layers concatenated as inputs for subsequent layers. Moreover, the data augmentation, parametric rectified linear unit, dynamic learning rate, batch normalization, and regularization (including dropout and L2) methods are used to increase classification accuracy and prevent overfitting. These attributes allow the SSCC framework to achieve good performance for HSI classification within 20 epochs. Three well-known datasets including Indiana Pines, University of Pavia, and Pavia Center were employed to evaluate the classification performance of the proposed algorithm. The GF-5 dataset of Anxin County, obtained from China's recently launched spaceborne Advanced Hyperspectral Imager, was also used for classification experiments. The experimental results demonstrate that the proposed SSCC framework achieves state-of-the-art performance with better training efficiency than other deep learning methods.

关键词： 3d convolutional neural network convolutional LSTM cascaded structures spectral-spatial feature learning hyperspectral image classification

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Reviewing 3d convolutional neural network approaches for medical image segmentation

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HELIYON 2024年第6期10卷 e27398页

作者： Ilesanmi, Ademola E. Ilesanmi, Taiwo O. Ajayi, Babatunde O. Univ Penn 3710 Hamilton Walk6th Floor Philadelphia PA 19104 USA Natl Populat Commiss Abuja Nigeria Natl Astron Res Inst Thailand Chiang Mai 50180 Thailand

Background: convolutional neural networks (CNNs) assume pivotal roles in aiding clinicians in diagnosis and treatment decisions. The rapid evolution of imaging technology has established three-dimensional (3d) CNNs as a formidable framework for delineating organs and anomalies in medical images. The prominence of 3d CNN frameworks is steadily growing within medical image segmentation and classification. Thus, our proposition entails a comprehensive review, encapsulating diverse 3d CNN algorithms for the segmentation of medical image anomalies and organs. Methods: This study systematically presents an exhaustive review of recent 3d CNN methodologies. Rigorous screening of abstracts and titles were carried out to establish their relevance. Research papers disseminated across academic repositories were meticulously chosen, analyzed, and appraised against specific criteria. Insights into the realm of anomalies and organ segmentation were derived, encompassing details such as network architecture and achieved accuracies. Results: This paper offers an all -encompassing analysis, unveiling the prevailing trends in 3d CNN segmentation. In-depth elucidations encompass essential insights, constraints, observations, and avenues for future exploration. A discerning examination indicates the preponderance of the encoder -decoder network in segmentation tasks. The encoder -decoder framework affords a coherent methodology for the segmentation of medical images. Conclusion: The findings of this study are poised to find application in clinical diagnosis and therapeutic interventions. despite inherent limitations, CNN algorithms showcase commendable accuracy levels, solidifying their potential in medical image segmentation and classification endeavors.

关键词： 3d convolutional neural network Medical images Segmentation of abnormalities and organs

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