The subcellular localization of human proteins is vital for understanding the structure of human cells.Proteins play a significant role within human cells,as many different groups of proteins are located in a specific...The subcellular localization of human proteins is vital for understanding the structure of human cells.Proteins play a significant role within human cells,as many different groups of proteins are located in a specific location to perform a particular function.Understanding these functions will help in discoveringmany diseases and developing their treatments.The importance of imaging analysis techniques,specifically in proteomics research,is becoming more prevalent.Despite recent advances in deep learning techniques for analyzing microscopy images,classification models have faced critical challenges in achieving high performance.Most protein subcellular images have a significant class imbalance.We use oversampling and under sampling techniques in this research to overcome this issue.We have used a Convolutional Neural Network(CNN)model called GapNet-PL for the multi-label classification task on the Human Protein Atlas Classification(HPA)Dataset.Authors have found that the ParametricRectified LinearUnit(PreLU)activation function is better than the Scaled Exponential LinearUnit(SeLU)activation function in the GapNet-PL model in most classification metrics.The results showed that the GapNet-PL model with the PReLU activation function achieved an area under the ROC curve(AUC)equal to 0.896,an F1 score of 0.541,and a recall of 0.473.展开更多
为了研究出一种快速、高效的玉米病害识别方法,针对玉米叶片病害识别问题,本文以灰斑病、南方锈病、小斑病、锈病、叶斑等5种常见的玉米叶片病害为研究对象,提出一种基于改进ResNet18神经网络的玉米病害识别方法。通过在ResNet18网络的...为了研究出一种快速、高效的玉米病害识别方法,针对玉米叶片病害识别问题,本文以灰斑病、南方锈病、小斑病、锈病、叶斑等5种常见的玉米叶片病害为研究对象,提出一种基于改进ResNet18神经网络的玉米病害识别方法。通过在ResNet18网络的基础上引入金字塔卷积(Pyramidal Convolution)可以在玉米复杂的生长环境中利用多尺度的特征信息来提高模型对单叶片的识别和定位能力,以有效加快模型的收敛速度并显著提高模型的病害识别准确率;将残差结构的激活函数替换为PReLU(Parametric Rectified Linear Unit)激活函数避免模型训练过程中的神经元死亡。在收集的真实玉米叶片病害数据集上进行的实验表明,与原始ResNet18残差网络相比,本文提出的模型在玉米叶片病害识别的准确率、精确度、召回率、F1分数分别提升了1.86%、1.78%、1.78%、1.87%;模型的参数尺寸减小了1.85%。该模型可作为一种检测复杂生长环境下玉米叶片病害的有效方法。展开更多
文摘The subcellular localization of human proteins is vital for understanding the structure of human cells.Proteins play a significant role within human cells,as many different groups of proteins are located in a specific location to perform a particular function.Understanding these functions will help in discoveringmany diseases and developing their treatments.The importance of imaging analysis techniques,specifically in proteomics research,is becoming more prevalent.Despite recent advances in deep learning techniques for analyzing microscopy images,classification models have faced critical challenges in achieving high performance.Most protein subcellular images have a significant class imbalance.We use oversampling and under sampling techniques in this research to overcome this issue.We have used a Convolutional Neural Network(CNN)model called GapNet-PL for the multi-label classification task on the Human Protein Atlas Classification(HPA)Dataset.Authors have found that the ParametricRectified LinearUnit(PreLU)activation function is better than the Scaled Exponential LinearUnit(SeLU)activation function in the GapNet-PL model in most classification metrics.The results showed that the GapNet-PL model with the PReLU activation function achieved an area under the ROC curve(AUC)equal to 0.896,an F1 score of 0.541,and a recall of 0.473.
文摘为了研究出一种快速、高效的玉米病害识别方法,针对玉米叶片病害识别问题,本文以灰斑病、南方锈病、小斑病、锈病、叶斑等5种常见的玉米叶片病害为研究对象,提出一种基于改进ResNet18神经网络的玉米病害识别方法。通过在ResNet18网络的基础上引入金字塔卷积(Pyramidal Convolution)可以在玉米复杂的生长环境中利用多尺度的特征信息来提高模型对单叶片的识别和定位能力,以有效加快模型的收敛速度并显著提高模型的病害识别准确率;将残差结构的激活函数替换为PReLU(Parametric Rectified Linear Unit)激活函数避免模型训练过程中的神经元死亡。在收集的真实玉米叶片病害数据集上进行的实验表明,与原始ResNet18残差网络相比,本文提出的模型在玉米叶片病害识别的准确率、精确度、召回率、F1分数分别提升了1.86%、1.78%、1.78%、1.87%;模型的参数尺寸减小了1.85%。该模型可作为一种检测复杂生长环境下玉米叶片病害的有效方法。
