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Alzheimer's disease diagnosis by 3d-SEConvNeXt

JOURNAL OF BIG dATA 2025年第1期12卷 1-13页

作者： Hu, Zhongyi Wang, Yuhang Xiao, Lei Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Wenzhou Univ Key Lab Intelligence Image Proc & Anal Wenzhou 325035 Peoples R China

Alzheimer's disease (Ad) constitutes a fatal neurodegenerative disorder and represents the most prevalent form of dementia among the elderly population. Traditional manual Ad classification methods, such as clinical diagnosis, are known to be time-consuming and labor-intensive, with relatively low accuracy. Therefore, our work aims to develop a new deep learning framework to tackle this challenge. Our proposed model integrates ConvNeXt with three-dimensional (3d) convolution and incorporates a 3d Squeeze-and-Excitation (3d-SE) attention mechanism to enhance early classification of Ad. The experimental data is sourced from the publicly accessible Alzheimer's disease Neuroimaging Initiative (AdNI) database, with raw Magnetic Resonance Imaging (MRI) data preprocessed using SPM12 software. Subsequently, the preprocessed data is input into the 3d-SEConvNeXt network to perform four classification tasks: distinguishing between Ad and Normal Control (NC), Mild Cognitive Impairment (MCI) and NC, Ad and MCI, as well as Ad, MCI, and NC. The experimental results indicate that the 3d-SEConvNeXt model consistently outperforms alternative models in terms of accuracy, achieving commendable outcomes in early Ad diagnostic tasks.

关键词： Alzheimer's disease Computer-aided diagnosis 3d convolutional neural network Image classification

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Improved Video Action Recognition Based on Pyramid Pooling and dual-Stream C3d networks

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APPLIEd SCIENCES-BASEL 2025年第8期15卷 4454-4454页

作者： Tan, Yuzhe Fu, Xueliang Li, Honghui Inner Mongolia Agr Univ Coll Comp & Informat Engn Hohhot 010018 Peoples R China

This paper proposes an innovative video behaviour classification method based on pyramid pooling and a variable-scale training strategy, which aims to improve the video behaviour-recognition performance of a 3d convolutional neural network (3d-CNN) and a dual-stream C3d network. By introducing pyramid pooling and secondary pooling operations, the number of pooling layers is optimised, the number of parameters of the model is significantly reduced, and the recognition accuracy is effectively improved. In the improved dual-stream C3d network, the early fusion strategy is adopted to better combine the spatio-temporal features and improve the accuracy of the model. In addition, by introducing the optical flow feature, the model's perception ability of video dynamic information is enhanced, and the recognition performance is further improved. Experimental results show that the proposed method performs well on multiple video datasets, which is better than the existing mainstream methods, which proves the innovation and efficiency of the proposed method in the field of video behaviour recognition.

关键词： video behaviour recognition 3d convolutional neural network pyramid pooling dual-stream C3d network

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Manufacturing Feature Recognition With a Sparse Voxel-Based convolutional neural network

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JOURNAL OF COMPUTING ANd INFORMATION SCIENCE IN ENGINEERING 2025年第3期25卷 031002页

作者： Vatandoust, Farzad Yan, Xiaoliang Rosen, david Melkote, Shreyes N. Georgia Inst Technol Sch Mech Engn Atlanta GA 30332 USA

Automated manufacturing feature recognition is a crucial link between computer-aided design and manufacturing, facilitating process selection and other downstream tasks in computer-aided process planning. While various methods such as graph-based, rule-based, and neural networks have been proposed for automatic feature recognition, they suffer from poor scalability or computational inefficiency. Recently, voxel-based convolutional neural networks have shown promise in solving these challenges but incur a tradeoff between computational cost and feature resolution. This paper investigates a computationally efficient sparse voxel-based convolutional neural network for manufacturing feature recognition, specifically, an octree-based sparse voxel convolutional neural network. This model is trained on a large-scale manufacturing feature dataset, and its performance is compared to a voxel-based feature recognition model (FeatureNet). The results indicate that the octree-based model yields higher feature recognition accuracy (99.5% on the test dataset) with 44% lower graphics processing unit (GPU) memory consumption than a voxel-based model of comparable resolution. In addition, increasing the resolution of the octree-based model enables recognition of finer manufacturing features. These results indicate that a sparse voxel-based convolutional neural network is a computationally efficient deep learning model for manufacturing feature recognition to enable process planning automation. Moreover, the sparse voxel-based neural network demonstrated comparable performance to a boundary representation-based feature recognition neural network, achieving similar accuracy in single-feature recognition without having access to the exact 3d shape descriptors.

关键词： 3d convolutional neural network sparse voxel representation manufacturing feature recognition computer-aided process planning (CAPP) artificial intelligence cybermanufacturing manufacturing automation

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Image-Based Flow Prediction of Vocal Folds Using 3d convolutional neural networks

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Journal of Bionic Engineering 2024年第2期21卷 991-1002页

作者： Yang Zhang Tianmei Pu Jiasen Xu Chunhua Zhou College of Astronautics Nanjing University of Aeronautics and AstronauticsNanjing210016China College of Electrical Energy and Power EngineeringYangzhou UniversityYangzhou225127China College of Aeronautics Nanjing University of Aeronautics and AstronauticsNanjing210016China

In this work,a three dimensional(3d)convolutional neural network(CNN)model based on image slices of various normal and pathological vocal folds is proposed for accurate and efficient prediction of glottal *** 3d CNN model is composed of the feature extraction block and regression *** feature extraction block is capable of learning low dimensional features from the high dimensional image data of the glottal shape,and the regression block is employed to flatten the output from the feature extraction block and obtain the desired glottal flow *** input image data is the condensed set of 2d image slices captured in the axial plane of the 3d vocal folds,where these glottal shapes are synthesized based on the equations of normal vibration *** output flow data is the corresponding flow rate,averaged glottal pressure and nodal pressure distributions over the glottal *** 3d CNN model is built to establish the mapping between the input image data and output flow *** ground-truth flow variables of each glottal shape in the training and test datasets are obtained by a high-fidelity sharp-interface immersed-boundary *** proposed model is trained to predict the concerned flow variables for glottal shapes in the test *** present 3d CNN model is more efficient than traditional Computational Fluid dynamics(CFd)models while the accuracy can still be retained,and more powerful than previous data-driven prediction models because more details of the glottal flow can be *** prediction performance of the trained 3d CNN model in accuracy and efficiency indicates that this model could be promising for future clinical applications.

关键词： Vocal folds Computational fluid dynamics Machine learning 3d convolutional neural network

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Fully automated 2d and 3d convolutional neural networks pipeline for video segmentation and myocardial infarction detection in echocardiography

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MULTIMEdIA TOOLS ANd APPLICATIONS 2022年第26期81卷 37417-37439页

作者： Hamila, Oumaima Ramanna, Sheela Henry, Christopher J. Kiranyaz, Serkan Hamila, Ridha Mazhar, Rashid Hamid, Tahir Univ Winnipeg Dept Appl Comp Sci 515 Portage Ave Winnipeg MB R3B 2E9 Canada Qatar Univ Dept Elect Engn Doha Qatar Hamad Med Corp Hamad Hosp Thorac Surg Doha Qatar Hamad Med Corp Heart Hosp Cardiol Doha Qatar

Myocardial infarction (MI) is a life-threatening disorder that occurs due to a prolonged limitation of blood supply to the heart muscles, and which requires an immediate diagnosis to prevent death. To detect MI, cardiologists utilize in particular echocardiography, which is a non-invasive cardiac imaging that generates real-time visualization of the heart chambers and the motion of the heart walls. These videos enable cardiologists to identify almost immediately regional wall motion abnormalities (RWMA) of the left ventricle (LV) chamber, which are highly correlated with MI. However, data acquisition is usually performed during emergency which results in poor-quality and noisy data that can affect the accuracy of the diagnosis. To address the identified problems, we propose in this paper an innovative, real-time and fully automated model based on convolutional neural networks (CNN) to early detect MI in a patient's echocardiography. Our model is a pipeline consisting of a 2d CNN that performs data preprocessing by segmenting the LV chamber from the apical four-chamber (A4C) view, followed by a 3d CNN that performs a binary classification to detect MI. The pipeline was trained and tested on the HMC-QU dataset consisting of 162 echocardiography. The 2d CNN achieved 97.18% accuracy on data segmentation, and the 3d CNN achieved 90.9% accuracy, 100% precision, 95% recall, and 97.2% F1 score. Our detection results outperformed existing state-of-the-art models that were tested on the HMC-QU dataset for MI detection. This work demonstrates that developing a fully automated system for LV segmentation and MI detection is efficient and propitious.

关键词： 3d convolutional neural network Video segmentation Myocardial infarction detection Echocardiography

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deep learning approaches using 2d and 3d convolutional neural networks for generating male pelvic synthetic computed tomography from magnetic resonance imaging

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MEdICAL PHYSICS 2019年第9期46卷 3788-3798页

作者： Fu, Jie Yang, Yingli Singhrao, Kamal Ruan, dan Chu, Fang-I Low, daniel A. Lewis, John H. Univ Calif Los Angeles David Geffen Sch Med 10833 Le Conte Ave Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Radiat Oncol 200 Suite B265Med Plaza Driveway Los Angeles CA 90095 USA

Purpose The improved soft tissue contrast of magnetic resonance imaging (MRI) compared to computed tomography (CT) makes it a useful imaging modality for radiotherapy treatment planning. Even when MR images are acquired for treatment planning, the standard clinical practice currently also requires a CT for dose calculation and x-ray-based patient positioning. This increases workloads, introduces uncertainty due to the required inter-modality image registrations, and involves unnecessary irradiation. While it would be beneficial to use exclusively MR images, a method needs to be employed to estimate a synthetic CT (sCT) for generating electron density maps and patient positioning reference images. We investigated 2d and 3d convolutional neural networks (CNNs) to generate a male pelvic sCT using a T1-weighted MR image and compare their performance. Methods A retrospective study was performed using CTs and T1-weighted MR images of 20 prostate cancer patients. CTs were deformably registered to MR images to create CT-MR pairs for training networks. The proposed 2d CNN, which contained 27 convolutional layers, was modified from the state-of-the-art 2d CNN to save computational memory and prepare for building the 3d CNN. The proposed 2d and 3d models were trained from scratch to map intensities of T1-weighted MR images to CT Hounsfield Unit (HU) values. Each sCT was generated in a fivefold cross-validation framework and compared with the corresponding deformed CT (dCT) using voxel-wise mean absolute error (MAE). The sCT geometric accuracy was evaluated by comparing bone regions, defined by thresholding at 150 HU in the dCTs and the sCTs, using dice similarity coefficient (dSC), recall, and precision. To evaluate sCT patient positioning accuracy, bone regions in dCTs and sCTs were rigidly registered to the corresponding cone-beam CTs. The resulting paired Euler transformation vectors were compared by calculating translation vector distances and absolute differences of Euler

关键词： 3d convolutional neural network MRI synthetic CT

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Learning Spatial-Spectral-Temporal EEG Features With Recurrent 3d convolutional neural networks for Cross-Task Mental Workload Assessment

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IEEE TRANSACTIONS ON neural SYSTEMS ANd REHABILITATION ENGINEERING 2019年第1期27卷 31-42页

作者： Zhang, Pengbo Wang, Xue Zhang, Weihang Chen, Junfeng Tsinghua Univ Dept Precis Instrument State Key Lab Precis Measurement Technol & Instru Beijing 100084 Peoples R China

Mental workload assessment is essential for maintaining human health and preventing accidents. Most research on this issue is limited to a single task. However, cross-task assessment is indispensable for extending a pre trained model to new workload conditions. Because brain dynamics are complex across different tasks, it is difficult to propose efficient human-designed features based on prior knowledge. Therefore, this paper proposes a concatenated structure of deep recurrent and 3d convolutional neural networks (R3dCNNs) to learn EEG features across different tasks without prior knowledge. First, this paper adds frequency and time dimensions to EEG topographic maps based on a Morlet wavelet transformation. Then, R3dCNN is proposed to simultaneously learn EEG features from the spatial, spectral, and temporal dimensions. The proposed model is validated based on the EEG signals collected from 20 subjects. This paper employs a binary classification of low and high mental workload across spatial n-back and arithmetic tasks. The results show that the R3dCNN achieves an average accuracy of 88.9%, which is a significant increase compared with that of the state-of-the-art methods. In addition, the visualization of the convolutional layers demonstrates that the deep neural network can extract detailed features. These results indicate that R3dCNN is capable of identifying the mental workload levels for cross-task conditions.

关键词： Mental workload cross-task 3d convolutional neural network recurrent neural network EEG

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Action Recognition in Videos with Spatio-Temporal Fusion 3d convolutional neural networks

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PATTERN RECOGNITION ANd IMAGE ANALYSIS 2021年第3期31卷 580-587页

作者： Wang, Y. Shen, X. J. Chen, H. P. Sun, J. X. Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Coll Software Changchun 130012 Jilin Peoples R China

Feature extraction based traditional human action recognition algorithms are complicated, leading to low recognition accuracy. We present an algorithm for the recognition of human actions in videos based on spatio-temporal fusion using 3d convolutional neural networks (3d CNNs). The algorithm contains two subnetworks, which extract deep spatial information and temporal information, respectively, and bilinear fusion policy is applied to obtain the final fused spatio-temporal information. Spatial information is represented by a gradient feature, and the temporal information is represented by optical flow. The fused spatio-temporal information can retrieve deep features from multiple angles by constructing a new 3d CNNs. The proposed algorithm is compared with the current mainstream algorithms in the KTH and UCF101 datasets, showing effectiveness and high recognition accuracy.

关键词： video action recognition 3d convolutional neural network spatiotemporal information bilinear fusion

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Human Action Recognition using 3d convolutional neural networks with 3d Motion Cuboids in Surveillance Videos 1

Human Action Recognition using 3D Convolutional Neural Netwo...

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1st International Conference on Robotics and Smart Manufacturing (RoSMa)

作者： Arunnehru, J. Chamundeeswari, G. Bharathi, S. Prasanna SRM Inst Sci & Technol Dept CSE Vadapalani Campus Chennai 26 Tamil Nadu India SRM Inst Sci & Technol Dept ECE Vadapalani Campus Chennai 26 Tamil Nadu India

In recent days, suspicious action recognition is a significant topic in intelligent video surveillance and computer vision research. Action recognition methodologies are specially needed for surveillance systems which are required to prevent crimes and treacherous actions before occurring. In this paper, we present 3d - convolutional neural networks (3d-CNN) with 3d motion cuboid for action detection and recognizing in videos. The experiments are conducted on benchmark KTH and Weizmann dataset. The proposed method is compared with the existing methods in terms of accuracy. The results show that this approach is outperforms previously published results. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Human action recognition 3d convolutional neural network 3d motion information Temporal difference Classification

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Early Identification of Alzheimer's disease Using an Ensemble of 3d convolutional neural networks and Magnetic Resonance Imaging 9th

Early Identification of Alzheimer's Disease Using an Ensembl...

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9th International Conference on Brain-Inspired Cognitive Systems (BICS)

作者： Chen, Yuanyuan Jia, Haozhe Huang, Zhaowei Xia, Yong Northwestern Polytech Univ Sch Comp Sci & Engn Natl Engn Lab Integrated Aero Space Ground Ocean Big Data Applicat Technol Xian 710072 Peoples R China Univ Sydney Sch IT Biomed & Multimedia Informat Technol BMIT Res Grp Sydney NSW 2006 Australia

ISBN: (纸本)9783030005634;9783030005627

Alzheimer's disease (Ad) has become a nonnegligible global health threat and social problem as the world population ages. The ability to identify Ad subjects in an early stage will be increasingly important as disease modifying therapies for Ad are developed. In this paper, we propose an ensemble of 3d convolutional neural networks (en3dCNN) for automated identification of Ad patients from normal controls using structural magnetic resonance imaging (MRI). We first employ the anatomical automatic labeling (AAL) cortical parcellation map to obtain 116 cortical volumes, then use the samples extracted from each cortical volume to train a 3d convolutional neural network (CNN), and finally assemble the predictions made by well-performed 3d CNNs via majority voting to classify each subject. We evaluated our algorithm against six existing algorithms on 764 MRI scans selected from the Alzheimer's disease Neuroimaging Initiative (AdNI) database. Our results indicate that the proposed en3dCNN algorithm is able to achieve the state-of-the-art performance in early identification of Alzheimer's disease using structural MRI.

关键词： Alzheimer's disease Computer-aided diagnosis Ensemble learning 3d convolutional neural network

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