Breast cancer is one of the malignancies that endanger women’s health all over the world.Considering that there is some noise and edge blurring in breast pathological images,it is easier to extract shallow features o...Breast cancer is one of the malignancies that endanger women’s health all over the world.Considering that there is some noise and edge blurring in breast pathological images,it is easier to extract shallow features of noise and redundant information when VGG16 network is used,which is affected by its relative shallow depth and small convolution kernel.To improve the pathological diagnosis of breast cancers,we propose a classification method for benign and malignant tumors in the breast pathological images which is based on feature concatenation of VGG16 network.First,in order to improve the problems of small dataset size and unbalanced data samples,the original BreakHis dataset is processed by data augmentation technologies,such as geometric transformation and color enhancement.Then,to reduce noise and edge blurring in breast pathological images,we perform bilateral filtering and denoising on the original dataset and sharpen the edge features by Sobel operator,which makes the extraction of shallow features by VGG16 model more accurate.Based on transfer learning,the network model trained with the expanded dataset is called VGG16-1,and another model trained with the image denoising and sharpening and mixed with the original dataset is called VGG16-2.The features extracted by VGG16-1 and VGG16-2 are concatenated,and then classified by support vector machine.The final experimental results show that the average accuracy is 98.44%,98.89%,98.30%and 97.47%,respectively,when the proposed method is tested with the breast pathological images of 40×,100×,200×and 400×on BreakHis dataset.展开更多
Text extraction from images using the traditional techniques of image collecting,and pattern recognition using machine learning consume time due to the amount of extracted features from the images.Deep Neural Networks...Text extraction from images using the traditional techniques of image collecting,and pattern recognition using machine learning consume time due to the amount of extracted features from the images.Deep Neural Networks introduce effective solutions to extract text features from images using a few techniques and the ability to train large datasets of images with significant results.This study proposes using Dual Maxpooling and concatenating convolution Neural Networks(CNN)layers with the activation functions Relu and the Optimized Leaky Relu(OLRelu).The proposed method works by dividing the word image into slices that contain characters.Then pass them to deep learning layers to extract feature maps and reform the predicted words.Bidirectional Short Memory(BiLSTM)layers extractmore compelling features and link the time sequence fromforward and backward directions during the training phase.The Connectionist Temporal Classification(CTC)function calcifies the training and validation loss rates.In addition to decoding the extracted feature to reform characters again and linking them according to their time sequence.The proposed model performance is evaluated using training and validation loss errors on the Mjsynth and Integrated Argument Mining Tasks(IAM)datasets.The result of IAM was 2.09%for the average loss errors with the proposed dualMaxpooling and OLRelu.In the Mjsynth dataset,the best validation loss rate shrunk to 2.2%by applying concatenating CNN layers,and Relu.展开更多
以无人机遥感影像为数据源实施多类别车辆目标的快速、精准检测,在城市道路管理及智慧城市建设等领域有重要的应用价值。针对无人机遥感影像中存在的背景复杂、车辆目标分布密集交错等问题,本文提出一种基于单阶段回归方法的车辆检测模...以无人机遥感影像为数据源实施多类别车辆目标的快速、精准检测,在城市道路管理及智慧城市建设等领域有重要的应用价值。针对无人机遥感影像中存在的背景复杂、车辆目标分布密集交错等问题,本文提出一种基于单阶段回归方法的车辆检测模型。在特征提取网络中,以3×3小尺寸卷积核为基础构建带有自适应校正(Squeeze and Excitation,SE)通道注意力机制的特征提取层作为网络前三层,对小尺寸目标特征进行细粒度提取,以级联非对称卷积组构成后网络的后两层,通过更少的计算量来完成对大尺度目标的特征提取。在特征增强网络中,将所有尺度特征图融合为三层输出特征图,并利用自适应锚点框机制实现目标框定位。试验结果表明,本文提出的模型能够达到0.906的综合检测精度与31帧/秒的检测速度,并且对于多种背景下不同密集程度的汽车目标表现出良好的泛化能力。展开更多
基金the National Natural Science Foundation of China(No.62006073)。
文摘Breast cancer is one of the malignancies that endanger women’s health all over the world.Considering that there is some noise and edge blurring in breast pathological images,it is easier to extract shallow features of noise and redundant information when VGG16 network is used,which is affected by its relative shallow depth and small convolution kernel.To improve the pathological diagnosis of breast cancers,we propose a classification method for benign and malignant tumors in the breast pathological images which is based on feature concatenation of VGG16 network.First,in order to improve the problems of small dataset size and unbalanced data samples,the original BreakHis dataset is processed by data augmentation technologies,such as geometric transformation and color enhancement.Then,to reduce noise and edge blurring in breast pathological images,we perform bilateral filtering and denoising on the original dataset and sharpen the edge features by Sobel operator,which makes the extraction of shallow features by VGG16 model more accurate.Based on transfer learning,the network model trained with the expanded dataset is called VGG16-1,and another model trained with the image denoising and sharpening and mixed with the original dataset is called VGG16-2.The features extracted by VGG16-1 and VGG16-2 are concatenated,and then classified by support vector machine.The final experimental results show that the average accuracy is 98.44%,98.89%,98.30%and 97.47%,respectively,when the proposed method is tested with the breast pathological images of 40×,100×,200×and 400×on BreakHis dataset.
基金supported this project under the Fundamental Research Grant Scheme(FRGS)FRGS/1/2019/ICT02/UKM/02/9 entitled“Convolution Neural Network Enhancement Based on Adaptive Convexity and Regularization Functions for Fake Video Analytics”.This grant was received by Prof.Assis.Dr.S.N.H.Sheikh Abdullah,https://www.ukm.my/spifper/research_news/instrumentfunds.
文摘Text extraction from images using the traditional techniques of image collecting,and pattern recognition using machine learning consume time due to the amount of extracted features from the images.Deep Neural Networks introduce effective solutions to extract text features from images using a few techniques and the ability to train large datasets of images with significant results.This study proposes using Dual Maxpooling and concatenating convolution Neural Networks(CNN)layers with the activation functions Relu and the Optimized Leaky Relu(OLRelu).The proposed method works by dividing the word image into slices that contain characters.Then pass them to deep learning layers to extract feature maps and reform the predicted words.Bidirectional Short Memory(BiLSTM)layers extractmore compelling features and link the time sequence fromforward and backward directions during the training phase.The Connectionist Temporal Classification(CTC)function calcifies the training and validation loss rates.In addition to decoding the extracted feature to reform characters again and linking them according to their time sequence.The proposed model performance is evaluated using training and validation loss errors on the Mjsynth and Integrated Argument Mining Tasks(IAM)datasets.The result of IAM was 2.09%for the average loss errors with the proposed dualMaxpooling and OLRelu.In the Mjsynth dataset,the best validation loss rate shrunk to 2.2%by applying concatenating CNN layers,and Relu.
文摘以无人机遥感影像为数据源实施多类别车辆目标的快速、精准检测,在城市道路管理及智慧城市建设等领域有重要的应用价值。针对无人机遥感影像中存在的背景复杂、车辆目标分布密集交错等问题,本文提出一种基于单阶段回归方法的车辆检测模型。在特征提取网络中,以3×3小尺寸卷积核为基础构建带有自适应校正(Squeeze and Excitation,SE)通道注意力机制的特征提取层作为网络前三层,对小尺寸目标特征进行细粒度提取,以级联非对称卷积组构成后网络的后两层,通过更少的计算量来完成对大尺度目标的特征提取。在特征增强网络中,将所有尺度特征图融合为三层输出特征图,并利用自适应锚点框机制实现目标框定位。试验结果表明,本文提出的模型能够达到0.906的综合检测精度与31帧/秒的检测速度,并且对于多种背景下不同密集程度的汽车目标表现出良好的泛化能力。
