Neurodegeneration is the gradual deterioration and eventual death of brain cells,leading to progressive loss of structure and function of neurons in the brain and nervous system.Neurodegenerative disorders,such as Alz...Neurodegeneration is the gradual deterioration and eventual death of brain cells,leading to progressive loss of structure and function of neurons in the brain and nervous system.Neurodegenerative disorders,such as Alzheimer’s,Huntington’s,Parkinson’s,amyotrophic lateral sclerosis,multiple system atrophy,and multiple sclerosis,are characterized by progressive deterioration of brain function,resulting in symptoms such as memory impairment,movement difficulties,and cognitive decline.Early diagnosis of these conditions is crucial to slowing down cell degeneration and reducing the severity of the diseases.Magnetic resonance imaging(MRI)is widely used by neurologists for diagnosing brain abnormalities.The majority of the research in this field focuses on processing the 2D images extracted from the 3D MRI volumetric scans for disease diagnosis.This might result in losing the volumetric information obtained from the whole brain MRI.To address this problem,a novel 3D-CNN architecture with an attention mechanism is proposed to classify whole-brain MRI images for Alzheimer’s disease(AD)detection.The 3D-CNN model uses channel and spatial attention mechanisms to extract relevant features and improve accuracy in identifying brain dysfunctions by focusing on specific regions of the brain.The pipeline takes pre-processed MRI volumetric scans as input,and the 3D-CNN model leverages both channel and spatial attention mechanisms to extract precise feature representations of the input MRI volume for accurate classification.The present study utilizes the publicly available Alzheimer’s disease Neuroimaging Initiative(ADNI)dataset,which has three image classes:Mild Cognitive Impairment(MCI),Cognitive Normal(CN),and AD affected.The proposed approach achieves an overall accuracy of 79%when classifying three classes and an average accuracy of 87%when identifying AD and the other two classes.The findings reveal that 3D-CNN models with an attention mechanism exhibit significantly higher classification performance compared to other models,highlighting the potential of deep learning algorithms to aid in the early detection and prediction of AD.展开更多
This paper proposes a novel,efficient and affordable approach to detect the students’engagement levels in an e-learning environment by using webcams.Our method analyzes spatiotemporal features of e-learners’micro bo...This paper proposes a novel,efficient and affordable approach to detect the students’engagement levels in an e-learning environment by using webcams.Our method analyzes spatiotemporal features of e-learners’micro body gestures,which will be mapped to emotions and appropriate engagement states.The proposed engagement detection model uses a three-dimensional convolutional neural network to analyze both temporal and spatial information across video frames.We follow a transfer learning approach by using the C3D model that was trained on the Sports-1M dataset.The adopted C3D model was used based on two different approaches;as a feature extractor with linear classifiers and a classifier after applying fine-tuning to the pretrained model.Our model was tested and its performance was evaluated and compared to the existing models.It proved its effectiveness and superiority over the other existing methods with an accuracy of 94%.The results of this work will contribute to the development of smart and interactive e-learning systems with adaptive responses based on users’engagement levels.展开更多
Automated segmentation of brain tumors using Magnetic Resonance Imaging(MRI)data is critical in the analysis and monitoring of disease development.As a result,gliomas are aggressive and diverse tumors that may be spli...Automated segmentation of brain tumors using Magnetic Resonance Imaging(MRI)data is critical in the analysis and monitoring of disease development.As a result,gliomas are aggressive and diverse tumors that may be split into intra-tumoral groups by using effective and accurate segmentation methods.It is intended to extract characteristics from an image using the Gray Level Co-occurrence(GLC)matrix feature extraction method described in the proposed work.Using Convolutional Neural Networks(CNNs),which are commonly used in biomedical image segmentation,CNNs have significantly improved the precision of the state-of-the-art segmentation of a brain tumor.Using two segmentation networks,a U-Net and a 3D CNN,we present a major yet easy combinative technique that results in improved and more precise estimates.The U-Net and 3D CNN are used together in this study to get better and more accurate estimates of what is going on.Using the dataset,two models were developed and assessed to provide segmentation maps that differed fundamentally in terms of the segmented tumour sub-region.Then,the estimates was made by two separate models that were put together to produce the final prediction.In comparison to current state-of-the-art designs,the precision(percentage)was 98.35,98.5,and 99.4 on the validation set for tumor core,enhanced tumor,and whole tumor,respectively.展开更多
针对高光谱遥感图像分类中空间信息利用不充分、样本标记数量不足的问题,提出一种基于多尺度3D-CNN和卷积块注意力机制的高光谱图像分类方法。采用特征映射方式从不同感受野充分挖掘并融合高光谱图像的空间特征和光谱特征,对融合后的空...针对高光谱遥感图像分类中空间信息利用不充分、样本标记数量不足的问题,提出一种基于多尺度3D-CNN和卷积块注意力机制的高光谱图像分类方法。采用特征映射方式从不同感受野充分挖掘并融合高光谱图像的空间特征和光谱特征,对融合后的空谱特征进行卷积块注意力机制处理;通过残差思想构建深层网络,采用Dropout方法处理过拟合问题,最后通过Softmax分类器进行分类。在Indian Pines、Pavia University和Salinas Valley 3个高光谱数据集上进行大量实验,分类结果表明:所提方法优于其他经典方法。展开更多
Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Ou...Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Our review traces the evolution of CNN, emphasizing the adaptation and capabilities of the U-Net 3D model in automating seismic fault delineation with unprecedented accuracy. We find: 1) The transition from basic neural networks to sophisticated CNN has enabled remarkable advancements in image recognition, which are directly applicable to analyzing seismic data. The U-Net 3D model, with its innovative architecture, exemplifies this progress by providing a method for detailed and accurate fault detection with reduced manual interpretation bias. 2) The U-Net 3D model has demonstrated its superiority over traditional fault identification methods in several key areas: it has enhanced interpretation accuracy, increased operational efficiency, and reduced the subjectivity of manual methods. 3) Despite these achievements, challenges such as the need for effective data preprocessing, acquisition of high-quality annotated datasets, and achieving model generalization across different geological conditions remain. Future research should therefore focus on developing more complex network architectures and innovative training strategies to refine fault identification performance further. Our findings confirm the transformative potential of deep learning, particularly CNN like the U-Net 3D model, in geosciences, advocating for its broader integration to revolutionize geological exploration and seismic analysis.展开更多
文摘Neurodegeneration is the gradual deterioration and eventual death of brain cells,leading to progressive loss of structure and function of neurons in the brain and nervous system.Neurodegenerative disorders,such as Alzheimer’s,Huntington’s,Parkinson’s,amyotrophic lateral sclerosis,multiple system atrophy,and multiple sclerosis,are characterized by progressive deterioration of brain function,resulting in symptoms such as memory impairment,movement difficulties,and cognitive decline.Early diagnosis of these conditions is crucial to slowing down cell degeneration and reducing the severity of the diseases.Magnetic resonance imaging(MRI)is widely used by neurologists for diagnosing brain abnormalities.The majority of the research in this field focuses on processing the 2D images extracted from the 3D MRI volumetric scans for disease diagnosis.This might result in losing the volumetric information obtained from the whole brain MRI.To address this problem,a novel 3D-CNN architecture with an attention mechanism is proposed to classify whole-brain MRI images for Alzheimer’s disease(AD)detection.The 3D-CNN model uses channel and spatial attention mechanisms to extract relevant features and improve accuracy in identifying brain dysfunctions by focusing on specific regions of the brain.The pipeline takes pre-processed MRI volumetric scans as input,and the 3D-CNN model leverages both channel and spatial attention mechanisms to extract precise feature representations of the input MRI volume for accurate classification.The present study utilizes the publicly available Alzheimer’s disease Neuroimaging Initiative(ADNI)dataset,which has three image classes:Mild Cognitive Impairment(MCI),Cognitive Normal(CN),and AD affected.The proposed approach achieves an overall accuracy of 79%when classifying three classes and an average accuracy of 87%when identifying AD and the other two classes.The findings reveal that 3D-CNN models with an attention mechanism exhibit significantly higher classification performance compared to other models,highlighting the potential of deep learning algorithms to aid in the early detection and prediction of AD.
基金Makkah Digital Gate Initiatives funded this research work under Grant Number(MDP-IRI-8-2020).Emirate of Makkah Province and King Abdulaziz University,Jeddah,Saudi Arabia.https://science.makkah.kau.edu.sa/Default-101888-AR.
文摘This paper proposes a novel,efficient and affordable approach to detect the students’engagement levels in an e-learning environment by using webcams.Our method analyzes spatiotemporal features of e-learners’micro body gestures,which will be mapped to emotions and appropriate engagement states.The proposed engagement detection model uses a three-dimensional convolutional neural network to analyze both temporal and spatial information across video frames.We follow a transfer learning approach by using the C3D model that was trained on the Sports-1M dataset.The adopted C3D model was used based on two different approaches;as a feature extractor with linear classifiers and a classifier after applying fine-tuning to the pretrained model.Our model was tested and its performance was evaluated and compared to the existing models.It proved its effectiveness and superiority over the other existing methods with an accuracy of 94%.The results of this work will contribute to the development of smart and interactive e-learning systems with adaptive responses based on users’engagement levels.
基金This research is funded by Deanship of Scientific Research at Umm Al-Qura University,Grant Code:22UQU4281768DSR05.
文摘Automated segmentation of brain tumors using Magnetic Resonance Imaging(MRI)data is critical in the analysis and monitoring of disease development.As a result,gliomas are aggressive and diverse tumors that may be split into intra-tumoral groups by using effective and accurate segmentation methods.It is intended to extract characteristics from an image using the Gray Level Co-occurrence(GLC)matrix feature extraction method described in the proposed work.Using Convolutional Neural Networks(CNNs),which are commonly used in biomedical image segmentation,CNNs have significantly improved the precision of the state-of-the-art segmentation of a brain tumor.Using two segmentation networks,a U-Net and a 3D CNN,we present a major yet easy combinative technique that results in improved and more precise estimates.The U-Net and 3D CNN are used together in this study to get better and more accurate estimates of what is going on.Using the dataset,two models were developed and assessed to provide segmentation maps that differed fundamentally in terms of the segmented tumour sub-region.Then,the estimates was made by two separate models that were put together to produce the final prediction.In comparison to current state-of-the-art designs,the precision(percentage)was 98.35,98.5,and 99.4 on the validation set for tumor core,enhanced tumor,and whole tumor,respectively.
文摘针对高光谱遥感图像分类中空间信息利用不充分、样本标记数量不足的问题,提出一种基于多尺度3D-CNN和卷积块注意力机制的高光谱图像分类方法。采用特征映射方式从不同感受野充分挖掘并融合高光谱图像的空间特征和光谱特征,对融合后的空谱特征进行卷积块注意力机制处理;通过残差思想构建深层网络,采用Dropout方法处理过拟合问题,最后通过Softmax分类器进行分类。在Indian Pines、Pavia University和Salinas Valley 3个高光谱数据集上进行大量实验,分类结果表明:所提方法优于其他经典方法。
文摘Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Our review traces the evolution of CNN, emphasizing the adaptation and capabilities of the U-Net 3D model in automating seismic fault delineation with unprecedented accuracy. We find: 1) The transition from basic neural networks to sophisticated CNN has enabled remarkable advancements in image recognition, which are directly applicable to analyzing seismic data. The U-Net 3D model, with its innovative architecture, exemplifies this progress by providing a method for detailed and accurate fault detection with reduced manual interpretation bias. 2) The U-Net 3D model has demonstrated its superiority over traditional fault identification methods in several key areas: it has enhanced interpretation accuracy, increased operational efficiency, and reduced the subjectivity of manual methods. 3) Despite these achievements, challenges such as the need for effective data preprocessing, acquisition of high-quality annotated datasets, and achieving model generalization across different geological conditions remain. Future research should therefore focus on developing more complex network architectures and innovative training strategies to refine fault identification performance further. Our findings confirm the transformative potential of deep learning, particularly CNN like the U-Net 3D model, in geosciences, advocating for its broader integration to revolutionize geological exploration and seismic analysis.
