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  • Open Access


    Underground Disease Detection Based on Cloud Computing and Attention Region Neural Network

    Pinjie Xu2, Ce Li1,2,*, Liguo Zhang3,4, Feng Yang1,2, Jing Zheng1,5, Jingwu Feng2

    Journal on Artificial Intelligence, Vol.1, No.1, pp. 9-18, 2019, DOI:10.32604/jai.2019.06157

    Abstract Detecting the underground disease is very crucial for the roadbed health monitoring and maintenance of transport facilities, since it is very closely related to the structural health and reliability with the rapid development of road traffic. Ground penetrating radar (GPR) is widely used to detect road and underground diseases. However, it is still a challenging task due to data access anywhere, transmission security and data processing on cloud. Cloud computing can provide scalable and powerful technologies for large-scale storage, processing and dissemination of GPR data. Combined with cloud computing and radar detection technology, it is possible to locate the underground… More >

  • Open Access


    Convolution Neural Networks and Support Vector Machines for Automatic Segmentation of Intracoronary Optical Coherence Tomography

    Caining Zhang1, Huaguang Li2, Xiaoya Guo3, David Molony4, Xiaopeng Guo2, Habib Samady4, Don P. Giddens4,5, Lambros Athanasiou6, Rencan Nie2,*, Jinde Cao3,*, Dalin Tang1,*,7

    Molecular & Cellular Biomechanics, Vol.16, No.2, pp. 153-161, 2019, DOI:10.32604/mcb.2019.06873

    Abstract Cardiovascular diseases are closely associated with deteriorating atherosclerotic plaques. Optical coherence tomography (OCT) is a recently developed intravascular imaging technique with high resolution approximately 10 microns and could provide accurate quantification of coronary plaque morphology. However, tissue segmentation of OCT images in clinic is still mainly performed manually by physicians which is time consuming and subjective. To overcome these limitations, two automatic segmentation methods for intracoronary OCT image based on support vector machine (SVM) and convolutional neural network (CNN) were performed to identify the plaque region and characterize plaque components. In vivo IVUS and OCT coronary plaque data from 5… More >

  • Open Access


    Image Recognition of Breast Tumor Proliferation Level Based on Convolution Neural Network

    Junhao Yang1, Chunxiao Chen1,*, Qingyang Zang1, Jianfei Li1

    Molecular & Cellular Biomechanics, Vol.15, No.4, pp. 203-214, 2018, DOI:10.32604/mcb.2018.03824

    Abstract Pathological slide is increasingly applied in the diagnosis of breast tumors despite the issues of large amount of data, slow viewing and high subjectivity. To overcome these problems, a micrograph recognition method based on convolutional neural network is proposed for pathological slide of breast tumor. Combined with multi-channel threshold and watershed segmentation, a sample database including single cell, adhesive cell and invalid cell was established. Then, the convolution neural network with six layers is constructed, which has ability to classify the stained breast tumor cells with accuracy of more than 90%, and evaluate the proliferation level with relative error of… More >

  • Open Access


    Tumor Cell Identification in Ki-67 Images on Deep Learning

    Ruihan Zhang1,2, Junhao Yang1, Chunxiao Chen1,*

    Molecular & Cellular Biomechanics, Vol.15, No.3, pp. 177-187, 2018, DOI: 10.3970/mcb.2018.04292

    Abstract The proportion of cells staining for the nuclear antigen Ki-67 is an important predictive indicator for assessment of tumor cell proliferation and growth in routine pathological investigation. Instead of traditional scoring methods based on the experience of a trained laboratory scientist, deep learning approach can be automatically used to analyze the expression of Ki-67 as well. Deep learning based on convolutional neural networks (CNN) for image classification and single shot multibox detector (SSD) for object detection are used to investigate the expression of Ki-67 for assessment of biopsies from patients with breast cancer in this study. The results focus on… More >

  • Open Access


    Super-Resolution Reconstruction of Images Based on Microarray Camera

    Jiancheng Zou1,*, Zhengzheng Li1, Zhijun Guo1, Don Hong2

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 163-177, 2019, DOI:10.32604/cmc.2019.05795

    Abstract In the field of images and imaging, super-resolution (SR) reconstruction of images is a technique that converts one or more low-resolution (LR) images into a highresolution (HR) image. The classical two types of SR methods are mainly based on applying a single image or multiple images captured by a single camera. Microarray camera has the characteristics of small size, multi views, and the possibility of applying to portable devices. It has become a research hotspot in image processing. In this paper, we propose a SR reconstruction of images based on a microarray camera for sharpening and registration processing of array… More >

  • Open Access


    A Recommendation System Based on Fusing Boosting Model and DNN Model

    Aziguli Wulam1,2, Yingshuai Wang1,2, Dezheng Zhang1,2,*, Jingyue Sang3, Alan Yang4

    CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1003-1013, 2019, DOI:10.32604/cmc.2019.07704

    Abstract In recent years, the models combining traditional machine learning with the deep learning are applied in many commodity recommendation practices. It has been proved better performance by the means of the neural network. Feature engineering has been the key to the success of many click rate estimation model. As we know, neural networks are able to extract high-order features automatically, and traditional linear models are able to extract low-order features. However, they are not necessarily efficient in learning all types of features. In traditional machine learning, gradient boosting decision tree is a typical representative of the tree model, which can… More >

  • Open Access


    R2N: A Novel Deep Learning Architecture for Rain Removal from Single Image

    Yecai Guo1,2,*, Chen Li1,2, Qi Liu3

    CMC-Computers, Materials & Continua, Vol.58, No.3, pp. 829-843, 2019, DOI:10.32604/cmc.2019.03729

    Abstract Visual degradation of captured images caused by rainy streaks under rainy weather can adversely affect the performance of many open-air vision systems. Hence, it is necessary to address the problem of eliminating rain streaks from the individual rainy image. In this work, a deep convolution neural network (CNN) based method is introduced, called Rain-Removal Net (R2N), to solve the single image de-raining issue. Firstly, we decomposed the rainy image into its high-frequency detail layer and low-frequency base layer. Then, we used the high-frequency detail layer to input the carefully designed CNN architecture to learn the mapping between it and its… More >

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