In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue...In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue anomalies.Traditionally,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of data.To address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans.This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods.There are three stages for learning;in the first stage,the whole dataset is used to learn the features.In the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented dataset.This method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for analysis.Various hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning process.HWBA-dataset registers maximum classification performance.We evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.展开更多
本研究利用空间金字塔池化(Spatial Pyramid Pooling,SPP)和全局平均池化(Global Average Pooling,GAP)优化传统的AlexNet架构,并将其应用于无人驾驶汽车的视觉识别系统中。这项研究旨在提高无人驾驶车辆通过摄像头感知环境的准确性和...本研究利用空间金字塔池化(Spatial Pyramid Pooling,SPP)和全局平均池化(Global Average Pooling,GAP)优化传统的AlexNet架构,并将其应用于无人驾驶汽车的视觉识别系统中。这项研究旨在提高无人驾驶车辆通过摄像头感知环境的准确性和效率。首先,笔者对AlexNet算法进行了改进,集成了SPP和GAP。SPP的引入使网络能够更有效地处理不同尺寸的图像,得到改进的AlexNet-SG网络,从而捕捉更多的空间信息。GAP的应用减少了模型的参数数量,从而降低了过拟合的风险并加快了训练速度。这些改进不仅增强了模型的泛化能力,还提高了网络对复杂场景的识别能力。本研究使用真实世界的交通环境数据对改进后的模型进行了测试,实验涵盖了多种交通场景,包括直车道、弯车道、人行道等。研究结果表明,AlexNet-SG在处理复杂交通场景时的表现明显优于原始模型,特别是在识别距离和准确率方面取得了显著提升。展开更多
本文针对滚动轴承故障诊断准确率不高的问题提出一种新方法。首先,将振动信号通过短时傅里叶变换(Short Time Fourier Transform,STFT)转化为时频图像构建数据集。其次,采用批量归一化算法和GeLU激活函数改进Alexnet网络,对不同工况的...本文针对滚动轴承故障诊断准确率不高的问题提出一种新方法。首先,将振动信号通过短时傅里叶变换(Short Time Fourier Transform,STFT)转化为时频图像构建数据集。其次,采用批量归一化算法和GeLU激活函数改进Alexnet网络,对不同工况的时频图像进行训练和故障诊断。在凯斯西储大学(Case Western Reserve University,CWRU)轴承数据集试验中,改进后的Alexnet网络训练损失更低,收敛速度更快,故障识别准确率更高。最后,比较模拟滚动轴承损伤故障实验平台采集的样本数据,改进Alexnet网络的故障识别准确率为97.2%,明显优于Alexnet网络、SVM网络和CNN网络,验证了该改进方法的有效性。展开更多
文摘In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue anomalies.Traditionally,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of data.To address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans.This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods.There are three stages for learning;in the first stage,the whole dataset is used to learn the features.In the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented dataset.This method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for analysis.Various hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning process.HWBA-dataset registers maximum classification performance.We evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.
文摘本研究利用空间金字塔池化(Spatial Pyramid Pooling,SPP)和全局平均池化(Global Average Pooling,GAP)优化传统的AlexNet架构,并将其应用于无人驾驶汽车的视觉识别系统中。这项研究旨在提高无人驾驶车辆通过摄像头感知环境的准确性和效率。首先,笔者对AlexNet算法进行了改进,集成了SPP和GAP。SPP的引入使网络能够更有效地处理不同尺寸的图像,得到改进的AlexNet-SG网络,从而捕捉更多的空间信息。GAP的应用减少了模型的参数数量,从而降低了过拟合的风险并加快了训练速度。这些改进不仅增强了模型的泛化能力,还提高了网络对复杂场景的识别能力。本研究使用真实世界的交通环境数据对改进后的模型进行了测试,实验涵盖了多种交通场景,包括直车道、弯车道、人行道等。研究结果表明,AlexNet-SG在处理复杂交通场景时的表现明显优于原始模型,特别是在识别距离和准确率方面取得了显著提升。
文摘本文针对滚动轴承故障诊断准确率不高的问题提出一种新方法。首先,将振动信号通过短时傅里叶变换(Short Time Fourier Transform,STFT)转化为时频图像构建数据集。其次,采用批量归一化算法和GeLU激活函数改进Alexnet网络,对不同工况的时频图像进行训练和故障诊断。在凯斯西储大学(Case Western Reserve University,CWRU)轴承数据集试验中,改进后的Alexnet网络训练损失更低,收敛速度更快,故障识别准确率更高。最后,比较模拟滚动轴承损伤故障实验平台采集的样本数据,改进Alexnet网络的故障识别准确率为97.2%,明显优于Alexnet网络、SVM网络和CNN网络,验证了该改进方法的有效性。
