针对传统面部识别技术中存在的诸多问题,如网络模型对关键通道特征的关注不足、参数量过大以及识别准确率不高等,本文提出了一种基于改进Visual Geometry Group 19(VGG19)模型的全新方案.该方案融合了U-Net网络架构的设计理念,并引入了...针对传统面部识别技术中存在的诸多问题,如网络模型对关键通道特征的关注不足、参数量过大以及识别准确率不高等,本文提出了一种基于改进Visual Geometry Group 19(VGG19)模型的全新方案.该方案融合了U-Net网络架构的设计理念,并引入了改进的SE Attention模块,以期提高模型的收敛速度和对面部细节的关注程度.在保持VGG19深层特征提取能力的基础上,通过特定设计的卷积层和跳跃连接,实现了对特征的高效融合与优化.经过改进的VGG19模型,不仅能更好地提取面部特征,还能在保证准确率的前提下,降低模型参数,提高运算效率.为了验证改进模型的效果,利用FER2013数据集和CK+两个数据集对本文提出的模型进行了测试.实验结果显示,改进后的VGG19网络在表情识别的准确率上分别取得了1.58%和4.04%的提升.这一结果充分证明了本文提出的方法在解决传统面部识别问题方面的优越性,也为面部识别技术的进一步发展提供了新的思路.展开更多
A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumor...A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumors.For diagnosed patients,their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans.Nowadays,Physicians and radiologists rely on Magnetic Resonance Imaging(MRI)pictures for their clinical evaluations of brain tumors.These evaluations are time-consuming,expensive,and require expertise with high skills to provide an accurate diagnosis.Scholars and industrials have recently partnered to implement automatic solutions to diagnose the disease with high accuracy.Due to their accuracy,some of these solutions depend on deep-learning(DL)methodologies.These techniques have become important due to their roles in the diagnosis process,which includes identification and classification.Therefore,there is a need for a solid and robust approach based on a deep-learning method to diagnose brain tumors.The purpose of this study is to develop an intelligent automatic framework for brain tumor diagnosis.The proposed solution is based on a novel dense dynamic residual self-attention transfer adaptive learning fusion approach(NDDRSATALFA),carried over two implemented deep-learning networks:VGG19 and UNET to identify and classify brain tumors.In addition,this solution applies a transfer learning approach to exchange extracted features and data within the two neural networks.The presented framework is trained,validated,and tested on six public datasets of MRIs to detect brain tumors and categorize these tumors into three suitable classes,which are glioma,meningioma,and pituitary.The proposed framework yielded remarkable findings on variously evaluated performance indicators:99.32%accuracy,98.74%sensitivity,98.89%specificity,99.01%Dice,98.93%Area Under the Curve(AUC),and 99.81%F1-score.In addition,a comparative analysis with recent state-of-the-art methods was performed and according to the comparative analysis,NDDRSATALFA shows an admirable level of reliability in simplifying the timely identification of diverse brain tumors.Moreover,this framework can be applied by healthcare providers to assist radiologists,pathologists,and physicians in their evaluations.The attained outcomes open doors for advanced automatic solutions that improve clinical evaluations and provide reasonable treatment plans.展开更多
Coffee is an important agricultural commodity,and its production is threatened by various diseases.It is also a source of concern for coffee-exporting countries,which is causing them to rethink their strategies for th...Coffee is an important agricultural commodity,and its production is threatened by various diseases.It is also a source of concern for coffee-exporting countries,which is causing them to rethink their strategies for the future.Maintaining crop production requires early diagnosis.Notably,Coffee Leaf Miner(CLM)Machine learning(ML)offers promising tools for automated disease detection.Early detection of CLM is crucial for minimising yield losses.However,this study explores the effectiveness of using Convolutional Neural Networks(CNNs)with transfer learning algorithms ResNet50,DenseNet121,MobileNet,Inception,and hybrid VGG19 for classifying coffee leaf images as healthy or CLM-infected.Leveraging the JMuBEN1 dataset,the proposed hybrid VGG19 model achieved exceptional performance,reaching 97%accuracy on both training and validation data.Additionally,high scores for precision,recall,and F1-score.The confusion matrix shows that all the test samples were correctly classified,which indicates the model’s strong performance on this dataset,demonstrating that the model is effective in distinguishing between healthy and CLM-infected leaves.This suggests strong potential for implementing this approach in real-world coffee plantations for early disease detection and improved disease management,and adapting it for practical deployment in agricultural settings.As well as supporting farmers in detecting diseases using modern,inexpensive methods that do not require specialists,and utilising deep learning technologies.展开更多
文摘针对传统面部识别技术中存在的诸多问题,如网络模型对关键通道特征的关注不足、参数量过大以及识别准确率不高等,本文提出了一种基于改进Visual Geometry Group 19(VGG19)模型的全新方案.该方案融合了U-Net网络架构的设计理念,并引入了改进的SE Attention模块,以期提高模型的收敛速度和对面部细节的关注程度.在保持VGG19深层特征提取能力的基础上,通过特定设计的卷积层和跳跃连接,实现了对特征的高效融合与优化.经过改进的VGG19模型,不仅能更好地提取面部特征,还能在保证准确率的前提下,降低模型参数,提高运算效率.为了验证改进模型的效果,利用FER2013数据集和CK+两个数据集对本文提出的模型进行了测试.实验结果显示,改进后的VGG19网络在表情识别的准确率上分别取得了1.58%和4.04%的提升.这一结果充分证明了本文提出的方法在解决传统面部识别问题方面的优越性,也为面部识别技术的进一步发展提供了新的思路.
基金National Natural Science Foundation of China(No.62075177)the Open Research Fund of CAS Key Laboratory of Spectral Imaging Technology(No.LSIT202005W)+1 种基金the 111 Project(No.B17035)。
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.(GPIP:1055-829-2024).
文摘A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumors.For diagnosed patients,their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans.Nowadays,Physicians and radiologists rely on Magnetic Resonance Imaging(MRI)pictures for their clinical evaluations of brain tumors.These evaluations are time-consuming,expensive,and require expertise with high skills to provide an accurate diagnosis.Scholars and industrials have recently partnered to implement automatic solutions to diagnose the disease with high accuracy.Due to their accuracy,some of these solutions depend on deep-learning(DL)methodologies.These techniques have become important due to their roles in the diagnosis process,which includes identification and classification.Therefore,there is a need for a solid and robust approach based on a deep-learning method to diagnose brain tumors.The purpose of this study is to develop an intelligent automatic framework for brain tumor diagnosis.The proposed solution is based on a novel dense dynamic residual self-attention transfer adaptive learning fusion approach(NDDRSATALFA),carried over two implemented deep-learning networks:VGG19 and UNET to identify and classify brain tumors.In addition,this solution applies a transfer learning approach to exchange extracted features and data within the two neural networks.The presented framework is trained,validated,and tested on six public datasets of MRIs to detect brain tumors and categorize these tumors into three suitable classes,which are glioma,meningioma,and pituitary.The proposed framework yielded remarkable findings on variously evaluated performance indicators:99.32%accuracy,98.74%sensitivity,98.89%specificity,99.01%Dice,98.93%Area Under the Curve(AUC),and 99.81%F1-score.In addition,a comparative analysis with recent state-of-the-art methods was performed and according to the comparative analysis,NDDRSATALFA shows an admirable level of reliability in simplifying the timely identification of diverse brain tumors.Moreover,this framework can be applied by healthcare providers to assist radiologists,pathologists,and physicians in their evaluations.The attained outcomes open doors for advanced automatic solutions that improve clinical evaluations and provide reasonable treatment plans.
文摘Coffee is an important agricultural commodity,and its production is threatened by various diseases.It is also a source of concern for coffee-exporting countries,which is causing them to rethink their strategies for the future.Maintaining crop production requires early diagnosis.Notably,Coffee Leaf Miner(CLM)Machine learning(ML)offers promising tools for automated disease detection.Early detection of CLM is crucial for minimising yield losses.However,this study explores the effectiveness of using Convolutional Neural Networks(CNNs)with transfer learning algorithms ResNet50,DenseNet121,MobileNet,Inception,and hybrid VGG19 for classifying coffee leaf images as healthy or CLM-infected.Leveraging the JMuBEN1 dataset,the proposed hybrid VGG19 model achieved exceptional performance,reaching 97%accuracy on both training and validation data.Additionally,high scores for precision,recall,and F1-score.The confusion matrix shows that all the test samples were correctly classified,which indicates the model’s strong performance on this dataset,demonstrating that the model is effective in distinguishing between healthy and CLM-infected leaves.This suggests strong potential for implementing this approach in real-world coffee plantations for early disease detection and improved disease management,and adapting it for practical deployment in agricultural settings.As well as supporting farmers in detecting diseases using modern,inexpensive methods that do not require specialists,and utilising deep learning technologies.
