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  • Open Access


    Deep 3D-Multiscale DenseNet for Hyperspectral Image Classification Based on Spatial-Spectral Information

    Haifeng Song1, Weiwei Yang1,*, Haiyan Yuan2, Harold Bufford3

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1441-1458, 2020, DOI:10.32604/iasc.2020.011988

    Abstract There are two main problems that lead to unsatisfactory classification performance for hyperspectral remote sensing images (HSIs). One issue is that the HSI data used for training in deep learning is insufficient, therefore a deeper network is unfavorable for spatial-spectral feature extraction. The other problem is that as the depth of a deep neural network increases, the network becomes more prone to overfitting. To address these problems, a dual-channel 3D-Multiscale DenseNet (3DMSS) is proposed to boost the discriminative capability for HSI classification. The proposed model has several distinct advantages. First, the model consists of dual channels that can extract both… More >

  • Open Access


    Road Damage Detection and Classification Using Mask R-CNN with DenseNet Backbone

    Qiqiang Chen1, *, Xinxin Gan2, Wei Huang1, Jingjing Feng1, H. Shim3

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2201-2215, 2020, DOI:10.32604/cmc.2020.011191

    Abstract Automatic road damage detection using image processing is an important aspect of road maintenance. It is also a challenging problem due to the inhomogeneity of road damage and complicated background in the road images. In recent years, deep convolutional neural network based methods have been used to address the challenges of road damage detection and classification. In this paper, we propose a new approach to address those challenges. This approach uses densely connected convolution networks as the backbone of the Mask R-CNN to effectively extract image feature, a feature pyramid network for combining multiple scales features, a region proposal network… More >

  • Open Access


    Privacy Protection for Medical Images Based on DenseNet and Coverless Steganography

    Yun Tan1, Jiaohua Qin1, *, Hao Tang2, Xuyu Xiang1, Ling Tan2, Neal N. Xiong3

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1797-1817, 2020, DOI:10.32604/cmc.2020.010802

    Abstract With the development of the internet of medical things (IoMT), the privacy protection problem has become more and more critical. In this paper, we propose a privacy protection scheme for medical images based on DenseNet and coverless steganography. For a given group of medical images of one patient, DenseNet is used to regroup the images based on feature similarity comparison. Then the mapping indexes can be constructed based on LBP feature and hash generation. After mapping the privacy information with the hash sequences, the corresponding mapped indexes of secret information will be packed together with the medical images group and… More >

  • Open Access


    Empirical Comparisons of Deep Learning Networks on Liver Segmentation

    Yi Shen1, Victor S. Sheng1, 2, *, Lei Wang1, Jie Duan1, Xuefeng Xi1, Dengyong Zhang3, Ziming Cui1

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1233-1247, 2020, DOI:10.32604/cmc.2020.07450

    Abstract Accurate segmentation of CT images of liver tumors is an important adjunct for the liver diagnosis and treatment of liver diseases. In recent years, due to the great improvement of hard device, many deep learning based methods have been proposed for automatic liver segmentation. Among them, there are the plain neural network headed by FCN and the residual neural network headed by Resnet, both of which have many variations. They have achieved certain achievements in medical image segmentation. In this paper, we firstly select five representative structures, i.e., FCN, U-Net, Segnet, Resnet and Densenet, to investigate their performance on liver… More >

  • Open Access


    Automatic Detection of Aortic Dissection Based on Morphology and Deep Learning

    Yun Tan1, #, Ling Tan2, #, Xuyu Xiang1, *, Hao Tang2, *, Jiaohua Qin1, Wenyan Pan1

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1201-1215, 2020, DOI:10.32604/cmc.2020.07127

    Abstract Aortic dissection (AD) is a kind of acute and rapidly progressing cardiovascular disease. In this work, we build a CTA image library with 88 CT cases, 43 cases of aortic dissection and 45 cases of health. An aortic dissection detection method based on CTA images is proposed. ROI is extracted based on binarization and morphology opening operation. The deep learning networks (InceptionV3, ResNet50, and DenseNet) are applied after the preprocessing of the datasets. Recall, F1-score, Matthews correlation coefficient (MCC) and other performance indexes are investigated. It is shown that the deep learning methods have much better performance than the traditional… More >

  • Open Access


    Text Detection and Recognition for Natural Scene Images Using Deep Convolutional Neural Networks

    Xianyu Wu1, Chao Luo1, Qian Zhang2, Jiliu Zhou1, Hao Yang1, 3, *, Yulian Li1

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 289-300, 2019, DOI:10.32604/cmc.2019.05990

    Abstract Words are the most indispensable information in human life. It is very important to analyze and understand the meaning of words. Compared with the general visual elements, the text conveys rich and high-level moral information, which enables the computer to better understand the semantic content of the text. With the rapid development of computer technology, great achievements have been made in text information detection and recognition. However, when dealing with text characters in natural scene images, there are still some limitations in the detection and recognition of natural scene images. Because natural scene image has more interference and complexity than… More >

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