As ocular computer-aided diagnostic(CAD)tools become more widely accessible,many researchers are developing deep learning(DL)methods to aid in ocular disease(OHD)diagnosis.Common eye diseases like cataracts(CATR),glau...As ocular computer-aided diagnostic(CAD)tools become more widely accessible,many researchers are developing deep learning(DL)methods to aid in ocular disease(OHD)diagnosis.Common eye diseases like cataracts(CATR),glaucoma(GLU),and age-related macular degeneration(AMD)are the focus of this study,which uses DL to examine their identification.Data imbalance and outliers are widespread in fundus images,which can make it difficult to apply manyDL algorithms to accomplish this analytical assignment.The creation of efficient and reliable DL algorithms is seen to be the key to further enhancing detection performance.Using the analysis of images of the color of the retinal fundus,this study offers a DL model that is combined with a one-of-a-kind concoction loss function(CLF)for the automated identification of OHD.This study presents a combination of focal loss(FL)and correntropy-induced loss functions(CILF)in the proposed DL model to improve the recognition performance of classifiers for biomedical data.This is done because of the good generalization and robustness of these two types of losses in addressing complex datasets with class imbalance and outliers.The classification performance of the DL model with our proposed loss function is compared to that of the baseline models using accuracy(ACU),recall(REC),specificity(SPF),Kappa,and area under the receiver operating characteristic curve(AUC)as the evaluation metrics.The testing shows that the method is reliable and efficient.展开更多
针对背景复杂、尺度变化较大、被遮挡情况下机械外破隐患目标检测精度不高,容易出现错检、漏检的问题,文中提出了一种改进YOLOv7(you only look once version 7)的机械外破隐患目标检测算法。文章在检测头网络中添加Swin Transformer注...针对背景复杂、尺度变化较大、被遮挡情况下机械外破隐患目标检测精度不高,容易出现错检、漏检的问题,文中提出了一种改进YOLOv7(you only look once version 7)的机械外破隐患目标检测算法。文章在检测头网络中添加Swin Transformer注意力机制提高对多尺度特征的提取能力,然后在主干网络中将部分卷积模块替换为深度可分离卷积,降低模型运算成本,采用Focal-EIOU(Focal and enhanced intersection over union)损失函数优化预测框,最后引入Mish激活函数增强网络的泛化能力,提高模型在复杂背景、目标部分被遮挡情况下的检测性能。实验结果表明,改进后的算法较原YOLOv7在准确率、召回率和平均精度均值上分别提高了5.2%、10.6%和5.2%,较其他主流算法在检测精度和模型体积上有着明显的优势,验证了改进方法的有效性,为复杂场景下机械外破隐患目标的边缘识别提供算法支持。展开更多
为了有效应对玉米地杂草对玉米产量和品质的影响,实现玉米与杂草的快速、准确检测,提出了一种基于改进YOLOv8n(You Only Look Once Version 8 nano)的玉米与杂草检测模型。首先,提出了ACMConv(Accurate and Computationally Minimal Con...为了有效应对玉米地杂草对玉米产量和品质的影响,实现玉米与杂草的快速、准确检测,提出了一种基于改进YOLOv8n(You Only Look Once Version 8 nano)的玉米与杂草检测模型。首先,提出了ACMConv(Accurate and Computationally Minimal Convolution)新型卷积方式,显著减少了模型计算量,使模型更加轻量化;其次,使用SELU激活函数,引入非线性因素,有效缓解了梯度消失问题;最后,引入Focal Loss作为边界框损失函数,使模型更加容易收敛。实验结果表明,相较于原始YOLOv8n模型,改进后的YOLOv8n模型的平均精度均值提升了1.3百分点,计算量降低了7.3%,实现了对玉米与杂草的高效、准确检测。展开更多
基金supported by the Deanship of Scientific Research,Vice Presidency forGraduate Studies and Scientific Research,King Faisal University,Saudi Arabia[Grant No.3,363].
文摘As ocular computer-aided diagnostic(CAD)tools become more widely accessible,many researchers are developing deep learning(DL)methods to aid in ocular disease(OHD)diagnosis.Common eye diseases like cataracts(CATR),glaucoma(GLU),and age-related macular degeneration(AMD)are the focus of this study,which uses DL to examine their identification.Data imbalance and outliers are widespread in fundus images,which can make it difficult to apply manyDL algorithms to accomplish this analytical assignment.The creation of efficient and reliable DL algorithms is seen to be the key to further enhancing detection performance.Using the analysis of images of the color of the retinal fundus,this study offers a DL model that is combined with a one-of-a-kind concoction loss function(CLF)for the automated identification of OHD.This study presents a combination of focal loss(FL)and correntropy-induced loss functions(CILF)in the proposed DL model to improve the recognition performance of classifiers for biomedical data.This is done because of the good generalization and robustness of these two types of losses in addressing complex datasets with class imbalance and outliers.The classification performance of the DL model with our proposed loss function is compared to that of the baseline models using accuracy(ACU),recall(REC),specificity(SPF),Kappa,and area under the receiver operating characteristic curve(AUC)as the evaluation metrics.The testing shows that the method is reliable and efficient.
文摘针对背景复杂、尺度变化较大、被遮挡情况下机械外破隐患目标检测精度不高,容易出现错检、漏检的问题,文中提出了一种改进YOLOv7(you only look once version 7)的机械外破隐患目标检测算法。文章在检测头网络中添加Swin Transformer注意力机制提高对多尺度特征的提取能力,然后在主干网络中将部分卷积模块替换为深度可分离卷积,降低模型运算成本,采用Focal-EIOU(Focal and enhanced intersection over union)损失函数优化预测框,最后引入Mish激活函数增强网络的泛化能力,提高模型在复杂背景、目标部分被遮挡情况下的检测性能。实验结果表明,改进后的算法较原YOLOv7在准确率、召回率和平均精度均值上分别提高了5.2%、10.6%和5.2%,较其他主流算法在检测精度和模型体积上有着明显的优势,验证了改进方法的有效性,为复杂场景下机械外破隐患目标的边缘识别提供算法支持。
文摘为了有效应对玉米地杂草对玉米产量和品质的影响,实现玉米与杂草的快速、准确检测,提出了一种基于改进YOLOv8n(You Only Look Once Version 8 nano)的玉米与杂草检测模型。首先,提出了ACMConv(Accurate and Computationally Minimal Convolution)新型卷积方式,显著减少了模型计算量,使模型更加轻量化;其次,使用SELU激活函数,引入非线性因素,有效缓解了梯度消失问题;最后,引入Focal Loss作为边界框损失函数,使模型更加容易收敛。实验结果表明,相较于原始YOLOv8n模型,改进后的YOLOv8n模型的平均精度均值提升了1.3百分点,计算量降低了7.3%,实现了对玉米与杂草的高效、准确检测。
