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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Jiang, Huiyan Li, Zhongkuan Li, Siqi Zhou, Fucai Northeastern Univ Software Coll Shenyang 110819 Liaoning Peoples R China Northeastern Univ Sino Dutch Biomed & Informat Engn Sch Shenyang 110819 Liaoning Peoples R China

ISBN: (纸本)9781538666500

Accurate classification on pathological images is a significant research focus such as for non-Hodgkin lymphomas (NHL). To this end, this paper proposes a hierarchical classification model based on the labels' statistics for three NHL pathological images, including chronic lymphocytic leukemia (CLL), follicular lymphoma (FL) and mantle cell lymphoma (MCL). First, each pathological image is converted onto the grayscale channel and then divided into 130 non-overlapped patches with 100 x 100 pixels. Next, the sparse autoencoder (SAE), an unsupervised feature extraction method, is utilized to learn the representations of all patches and meanwhile texture features are extracted on these patches which are considered as the hand-craft features. Following this process, we can obtain a 680-dimension feature set. Finally, a hierarchical classification model trained by these 680-dimension features is applied to classify NHL as CLL, FL and MCL, where the label of each NHL pathological image is determined via the output labels of its 130 patches. The experimental results and comparisons demonstrate the advantages of the proposed hierarchical classification model.

关键词： Lymphoma pathological images sparse autoencoder Feature extraction Hierarchical classification

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Application of stack marginalised sparse denoising auto-encoder in fault diagnosis of rolling bearing

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JOURNAL OF ENGINEERING-JOE 2018年第16期2018卷 1772-1777页

作者： Zhang, Junling Chen, Zhigang Du, Xiaolei Xu, Xu Yu, Miao Beijing Univ Civil Engn & Architecture Inst Elect & Mech & Vehicle Engn Beijing 100044 Peoples R China Construct Safety Monitoring Engn Technol Res Ctr Beijing 100037 Peoples R China

When a fracturing vehicle is working, it generally needs to bear high loads, media corrosion and erosion. For this special working environment, this study proposes a rolling bearing fault diagnosis method based on stack marginalised sparse denoising auto-encoder (SDAE). This method combines the sparse auto-encoder (SAE) and the denoising auto-encoder (DAE) and combines the characteristics of dimensionality reduction and robustness. The method adds marginalisation to optimise the SDAE. Finally, it uses a two-layer stacking method. The output results of the second marginalised SDAE are used as input to the softmax classifier for learning training and classification testing. This improved method (stack SDAE) improves the denoising ability, reduces the computational complexity, solves the problems of difficult parameter adjustment and slows training convergence. The experimental tests were carried out on the failure of pitting corrosion of the outer ring of the bearing, pitting failure of the inner ring, and cracking of the rolling element. The results show that the algorithm can effectively improve the accuracy of fault diagnosis of rolling bearings, and it has greatly improved than the algorithms of SAEs and DAE.

关键词： rolling bearings fault diagnosis mechanical engineering computing computational complexity corrosion failure analysis cracks fracture signal denoising media corrosion erosion rolling bearing fault diagnosis method two-layer stacking method SDAE stack marginalised sparse denoising autoencoder application fracturing vehicle sparse autoencoder SAE denoising autoencoder DAE softmax classifier learning training classification testing computational complexity pitting corrosion failure cracking

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Hybrid evolutionary approach for Devanagari handwritten numeral recognition using Convolutional Neural Network 6

Hybrid evolutionary approach for Devanagari handwritten nume...

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6th International Conference on Smart Computing and Communications (ICSCC)

作者： Trivedi, Adarsh Srivastava, Siddhant Mishra, Apoorva Shukla, Anupam Tiwari, Ritu ABV Indian Inst Informat Technol Soft Comp & Expert Syst Lab Gwalior 474015 India

In recent years, deep learning has been extensively used in both supervised and unsupervised learning problems. Among the deep learning models, CNN has outperformed all others for object recognition task. Although CNN achieves exceptional accuracy, still a huge number of iterations and chances of getting stuck in local optima makes it computationally expensive to train. Genetic Algorithm is a metaheuristic approach inspired by the theory of natural selection and has been used for solving both bounded and unbounded optimization problems by a large success. To handle these issues, we have developed a hybrid deep learning model using Genetic Algorithm and L-BFGS method for training CNN. To test our model, we have taken the Devanagari handwritten numeral dataset. Our results show that GA assisted CNN produces better results than non-GA assisted CNN. This study concludes that evolutionary technique can be used to train CNN more efficiently. (C) 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 6th International Conference on Smart Computing and Communications.

关键词： Convolutional Neural Network Genetic Algorithm sparse autoencoder

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PAN-SHARPENING BASED ON MULTILEVEL COUPLED DEEP NETWORK 38

PAN-SHARPENING BASED ON MULTILEVEL COUPLED DEEP NETWORK

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38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Cai, Wanting Xu, Yang Wu, Zebin Liu, Hongyi Qian, Ling Wei, Zhihui Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Nanjing Robot Res Inst Co Ltd Nanjing 211135 Jiangsu Peoples R China Lianyungang E Port Informat Dev Co Ltd Lianyungang 222042 Peoples R China Nanjing Univ Sci & Technol Sch Sci Nanjing 210094 Jiangsu Peoples R China Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Jiangsu Peoples R China

ISBN: (纸本)9781538671504

Pan-sharpening is a common image-fusion method. To improve the quality of fused images, a multilevel deep learning Pan-sharpening method is proposed in this paper. In the training phase, we introduce Coupled sparse Denoising Autoencorder (CSDA) to reconstruct high-Resolution (HR) multispectral (MS) image from low-Resolution (LR) MS image and HR Panchromatic (Pan) image. CSDA has four networks including LM-HP network, HR-MS network, feature mapping network and fine-tuning network. The hidden features in LM-HP network and HR-MS network as well as the mapping function between the two features are learned through joint optimization. In LM-HP and HR-MS networks, the hidden features of image patch pairs are extracted by the sparse autoencoder. A sparse denoising autoencoder is used to build the nonlinear mapping between the extracted features. In the testing phase, the LR-MS and HR-Pan images patches are fed to the CSDA network to reconstruct the fused HR-MS image. The experimental results show that the proposed method is better than the traditional pans-sharpening methods.

关键词： Pan-sharpening deep learning sparse autoencoder multilevel coupled networks

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Class-Specific Pre-trained sparse autoencoders for Learning Effective Features for Document Classification 8

Class-Specific Pre-trained Sparse Autoencoders for Learning ...

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8th Computer Science and Electronic Engineering (CEEC)

作者： Abdulhussain, Maysa I. Gan, John Q. Univ Essex Sch Comp Sci & Elect Engn Colchester C04 3SQ Essex England Univ Baghdad Dept Comp Sci Coll Sci Baghdad Iraq

ISBN: (纸本)9781509020508

sparse autoencoder is a commonly used deep learning approach for automatically learning features from unlabelled data (unsupervised feature learning). This paper proposes class-specific (supervised) pre-trained approach based on sparse autoencoder to gain low-dimensional interesting structure of features with high performance in document classification. Experimental results have demonstrated the advantages and usefulness of the proposed method in document classification in high-dimensional feature space, in terms of the limited number of features required to achieve good classification accuracy.

关键词： sparse autoencoder deep learning feature learning document categorization

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Automatic diagnosis of pulmonary nodules using a hierarchical extreme learning machine model

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2018年第3期11卷 192-201页

作者： Hao, Rui Qiang, Zilin Qiang, Yan Ge, Lei Zhao, Juanjuan Shanxi Univ Finance & Econ Coll Informat Management Taiyuan Shanxi Peoples R China Taiyuan Univ Technol Coll Software Taiyuan Shanxi Peoples R China Taiyuan Univ Technol Coll Comp Sci & Technol Taiyuan Shanxi Peoples R China

To effectively improve the diagnosis of pulmonary nodules, this paper proposes a new automatic diagnosis method for pulmonary nodules based on a new hierarchical extreme learning machine (H-ELM) that can automatically carry out feature extraction, model training and pulmonary nodule detection. In our method, an adaptive histogram equalisation is used first to enhance contrast of the original pulmonary nodule image. The processed images are then input into an extreme learning machine (ELM)-based unsupervised multilayer auto-encoder to obtain more compact and meaningful high-level features of the pulmonary nodule image. Finally, supervised feature classification, which uses these high-level features of the pulmonary nodule as input data, is implemented using the ELM classifier. In the experiments, 2,800 pulmonary nodule images are used to validate the proposed method, and compared with existing pulmonary nodule diagnosis methods, our proposed method is more accurate and less time consuming and effectively avoids the complexity of manual feature extraction.

关键词： automatic diagnosis pulmonary nodule hierarchical extreme learning adaptive histogram equalisation sparse autoencoder feature extraction feature classification computer aided diagnosis

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Feature Abstraction for Early Detection of Multi-type of Dementia with sparse Auto-encoder

Feature Abstraction for Early Detection of Multi-type of Dem...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Alkabawi, Elham M. Hilal, Allaa R. Basir, Otman A. Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada

ISBN: (纸本)9781538616451

With millions of people suffering from dementia worldwide, the global prevalence of dementia has a significant impact on the patients' lives, their caregivers' physical and emotional states, and the global economy. Early diagnosis of dementia helps in finding suitable therapies that reduce or even prevent further deterioration of patients' cognitive abilities. MRI scans are shown to be the most effective strategy to enable early diagnosis of dementia. Nevertheless, the early diagnosis of dementia is a challenging task due to the high dimensionality of MRI scans that may degrade the effectiveness of machine learning models. Feature abstraction is a part of dimensionality reduction process need by researchers to represent input data in its simplest form that results in a more-robust system model. It is a technique that collects relevant features and ignores irrelevant or redundant ones from data without loss of much key information. This paper proposes a novel feature abstraction method using sparse autoencoder (SAE) to reduce the dimensionality of and extract key features from MRI neuroimages. These features and that obtained from the popular PCA approach are then used to train a Linear Discriminant Analysis (LDA) and Logistic Regression classifiers in order to compare their prediction accuracy. The experimental results show that the proposed approach yields higher classification accuracy compared to that using the PCA by 8 percent of classification accuracy. The experimental results show that the use of features learned by SAE in early diagnosis of multi-type dementia provides better classification performance than the use of raw image pixel intensities for diagnosing dementia.

关键词： Dimensionality Reduction Early diagnosis of Dementia Feature abstraction Machine learning PCA sparse autoencoder

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DETECTION OF LUMEN AND MEDIA-ADVENTITIA BORDERS IN IVUS IMAGES USING sparse AUTO-ENCODER NEURAL NETWORK 14

DETECTION OF LUMEN AND MEDIA-ADVENTITIA BORDERS IN IVUS IMAG...

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IEEE 14th International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro

作者： Su, Shengran Gao, Zhifan Zhang, Heye Lin, Qiang Hau, William Kongto Li, Shuo Zhejiang Univ Technol Coll Sci Hangzhou Zhejiang Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China Univ Chinese Acad Sci Shenzhen Coll Adv Technol Beijing Peoples R China Univ Hong Kong LiKaShing Fac Med Hong Kong Hong Kong Peoples R China Univ Western Ontario London ON Canada

ISBN: (纸本)9781509011728

This paper describes an artificial neural network (ANN) method that employs a feature-learning algorithm to detect the lumen and MA borders in intravascular ultrasound (IVUS) images. Three types of imaging features including spatial, neighboring, and gradient features were used as the input features to the neural network, and then the different vascular layers were distinguished using two sparse autoencoders and one softmax classifier. To smooth the lumen and MA borders detected by the ANN method, we used the active contour model. The performance of our approach was compared with the manual drawing method and another existing method on 538 IVUS images from six subjects. Results showed that our approach had a high correlation (r = 0.9284 +/- 0.9875 for all measurements) and good agreement (bias = 0.0148 +/- 0.4209 mm) with the manual drawing method, and small detection error (lumen border: 0.0928 +/- 0.0935 mm, MA border: 0.1056 +/- 0.1088 mm). The average time to process each image was 14 +/- 4.6 seconds. The obtained results indicate that our proposed approach can be used to efficiently and accurately detect the lumen and MA borders in IVUS images.

关键词： Intravascular image Deep neural network Image segmentation sparse autoencoder

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Deep feature learning for cover song identification

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第22期76卷 23225-23238页

作者： Fang, Jiunn-Tsair Day, Chi-Ting Chang, Pao-Chi Ming Chuan Univ Dept Elect Engn 5 Deming Rd Taoyuan 33348 Taiwan Natl Cent Univ Dept Commun Engn 300 Jhongda Rd Taoyuan 32001 Taiwan

The identification of a cover song, which is an alternative version of a previously recorded song, for music retrieval has received increasing attention. Methods for identifying a cover song typically involve comparing the similarity of chroma features between a query song and another song in the data set. However, considerable time is required for pairwise comparisons. In this study, chroma features were patched to preserve the melody. An intermediate representation was trained to reduce the dimension of each patch of chroma features. The training was performed using an autoencoder, commonly used in deep learning for dimensionality reduction. Experimental results showed that the proposed method achieved better accuracy for identification and spent less time for similarity matching in both covers80 dataset and Million Song Dataset as compared with traditional approaches.

关键词： Cover song Deep learning Music retrieval sparse autoencoder

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Feature learning and change feature classification based on deep learning for ternary change detection in SAR images

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2017年第Jul.期129卷 212-225页

作者： Gong, Maoguo Yang, Hailun Zhang, Puzhao Xidian Univ Minist Educ Int Res Ctr Intelligent Percept & Computat Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Provinc Peoples R China

Ternary change detection aims to detect changes and group the changes into positive change and negative change. It is of great significance in the joint interpretation of spatial-temporal synthetic aperture radar images. In this study, sparse autoencoder, convolutional neural networks (CNN) and unsupervised clustering are combined to solve ternary change detection problem without any supervison. Firstly, sparse autoencoder is used to transform log-ratio difference image into a suitable feature space for extracting key changes and suppressing outliers and noise. And then the learned features are clustered into three classes, which are taken as the pseudo labels for training a CNN model as change feature classifier. The reliable training samples for CNN are selected from the feature maps learned by sparse autoencoder with certain selection rules. Having training samples and the corresponding pseudo labels, the CNN model can be trained by using back propagation with stochastic gradient descent. During its training procedure, CNN is driven to learn the concept of change, and more powerful model is established to distinguish different types of changes. Unlike the traditional methods, the proposed framework integrates the merits of sparse autoencoder and CNN to learn more robust difference representations and the concept of change for ternary change detection. Experimental results on real datasets validate the effectiveness and superiority of the proposed framework. (C) 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： Ternary change detection Deep learning Synthetic aperture radar Representation learning sparse autoencoder Convolutional neural networks

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