由于皮肤黑色素癌图像存在类内差异大、样本数据集小等特点,采用深度残差网络可以有效解决训练过程中过拟合问题,提高识别准确率.但是深度残差网络模型的训练参数多,时间复杂度高.为了提高训练效率,提高识别准确率,首先从理论上分析了...由于皮肤黑色素癌图像存在类内差异大、样本数据集小等特点,采用深度残差网络可以有效解决训练过程中过拟合问题,提高识别准确率.但是深度残差网络模型的训练参数多,时间复杂度高.为了提高训练效率,提高识别准确率,首先从理论上分析了深度残差网络模型的结构,通过修改网络结构,利用Inception结构代替残差网络中的卷积层、池化层,减少模型的训练参数数量,降低时间复杂度.在此基础上,提出了基于Inception深度残差网络皮肤黑色素癌分类识别算法(Inception Deep Residual Network,IDRN),用Inception结构代替残差网络中的卷积池化层,用SeLU激活函数代替传统的ReLU函数.之后,在公开的黑色素癌皮肤镜图像ISIC2017数据集上进行实验验证.理论和实验表明,与传统的卷积神经网络ResNet50相比,本文提出的新的分类算法降低了时间复杂度,提高了识别准确率.展开更多
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
文摘弱小船舶目标实时检测因在海上搜救、无人船和海上交通管理等领域中的众多应用而备受关注。虽然基于深度学习的目标检测算法,如YOLO(you only look once)和SSD(single shot multibox detector)等取得了不错的目标检测性能,但是它们仍然无法实时有效检测出海上弱小船舶运动目标。针对此问题,文章提出了一种改进的深度学习网络结构,结合SELU(scaled exponential linear units)激活函数,有效解决了已有的YOLOv2算法对弱小目标检测率较低的不足以及YOLOv3算法中残差网络结构冗余的问题。实验表明,该文提出的方法在海上弱小船舶目标检测上,比原YOLO算法具有更高的检测精度、更快的检测速度和更优良的鲁棒性。该方法在低配硬件环境中仍具有实时性的特点,因此对算法的推广应用具有实际的意义。
文摘由于皮肤黑色素癌图像存在类内差异大、样本数据集小等特点,采用深度残差网络可以有效解决训练过程中过拟合问题,提高识别准确率.但是深度残差网络模型的训练参数多,时间复杂度高.为了提高训练效率,提高识别准确率,首先从理论上分析了深度残差网络模型的结构,通过修改网络结构,利用Inception结构代替残差网络中的卷积层、池化层,减少模型的训练参数数量,降低时间复杂度.在此基础上,提出了基于Inception深度残差网络皮肤黑色素癌分类识别算法(Inception Deep Residual Network,IDRN),用Inception结构代替残差网络中的卷积池化层,用SeLU激活函数代替传统的ReLU函数.之后,在公开的黑色素癌皮肤镜图像ISIC2017数据集上进行实验验证.理论和实验表明,与传统的卷积神经网络ResNet50相比,本文提出的新的分类算法降低了时间复杂度,提高了识别准确率.
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.
