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Brain Tumor Detection by Using stacked autoencoders in Deep Learning

JOURNAL OF MEDICAL SYSTEMS 2019年第2期44卷 1-12页

作者： Amin, Javaria Sharif, Muhammad Gul, Nadia Raza, Mudassar Anjum, Muhammad Almas Nisar, Muhammad Wasif Bukhari, Syed Ahmad Chan COMSATS Univ Islamabad Dept Comp Sci Wah Campus Islamabad Pakistan POF Hosp Wah Med Coll Dept Radiol Rawalpindi Punjab Pakistan NUST Coll EME Rawalpindi Punjab Pakistan St Johns Univ Collins Coll Profess Studies Div Comp Sci Math & Sci New York NY USA

Brain tumor detection depicts a tough job because of its shape, size and appearance variations. In this manuscript, a deep learning model is deployed to predict input slices as a tumor (unhealthy)/non-tumor (healthy). This manuscript employs a high pass filter image to prominent the inhomogeneities field effect of the MR slices and fused with the input slices. Moreover, the median filter is applied to the fused slices. The resultant slices quality is improved with smoothen and highlighted edges of the input slices. After that, based on these slices' intensity, a 4-connected seed growing algorithm is applied, where optimal threshold clusters the similar pixels from the input slices. The segmented slices are then supplied to the fine-tuned two layers proposed stacked sparse autoencoder (SSAE) model. The hyperparameters of the model are selected after extensive experiments. At the first layer, 200 hidden units and at the second layer 400 hidden units are utilized. The testing is performed on the softmax layer for the prediction of the images having tumors and no tumors. The suggested model is trained and checked on BRATS datasets i.e., 2012(challenge and synthetic), 2013, and 2013 Leaderboard, 2014, and 2015 datasets. The presented model is evaluated with a number of performance metrics which demonstrates the improved performance.

关键词： stacked sparse autoencoder Glioma Softmax Hidden size Magnetic resonance images

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Application of deep learning and near infrared spectroscopy in cereal analysis

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VIBRATIONAL SPECTROSCOPY 2020年第0期106卷 103009-000页

作者： Ba Tuan Le Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam

Deep learning is an important research achievement of artificial intelligence in recent years and has received special attention from scientists around the world. This study applies deep learning to spectral analysis techniques and proposes a rapid analysis method for cereals. First, the advanced features of the near infrared spectroscopy (NIR) were extracted by the deep learning-stacked sparse autoencoder (SSAE) method, and then the prediction model is built using the affine transformation (AT) and the extreme learning machine (ELM). Experiments were conducted on corn and rice data sets to verify the effectiveness of the method. The results show that the proposed method achieves good prediction results and is superior to other typical NIR analysis methods.

关键词： Deep learning Cereal analysis Near-Infrared spectroscopy stacked sparse autoencoder Extreme learning machine

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A Novel Order Analysis and stacked sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring

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SENSORS 2020年第10期20卷 2878-2878页

作者： Ou, Jiayu Li, Hongkun Huang, Gangjin Zhou, Qiang Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China

Milling is a main processing mode of the modern manufacturing industry, which seriously affects the quality and precision of the machined workpiece. However, it is difficult to monitor the tool wear condition in the continuous cutting process, especially under a variable speed condition. The existing tool wear condition monitoring methods only carry out analysis with a constant engine speed. Different from the general monitoring methods, this paper put forward a milling cutter wear condition monitoring method based on order analysis (OA) and stacked sparse autoencoder (SSAE). The methodology in the research include signals feature extraction and tool wear state monitoring and were designed to analyze the three-phase spindle current signals instead of the traditional force signals and vibration signals. The variable speed signals were transformed into angle domain stationary signals by order analysis, and the SSAE neural network was used to monitor the tool wear state. The proposed method was verified on the laboratory signals and the results showed a better performance than the other methods and a better applicability in actual industrial manufacturing.

关键词： order analysis stacked sparse autoencoder spindle current signals tool wear condition monitoring

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An Approach for Severity Prediction of Autism Using Machine Learning

An Approach for Severity Prediction of Autism Using Machine ...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Che, Min Wang, Liya Huang, Lin Jiang, Zhibin Shanghai Jiao Tong Univ Dept Ind Engn & Management Shanghai Peoples R China

ISBN: (纸本)9781728138046

Effective early diagnosis of autism can have a significant impact on its intervention and treatment. In this paper, an approach is proposed for comprehensively considering genetic factors and environmental factors to predict the severity of autism. According to the Childhood Autism Rating Scale (CARS), a sample set was collected from the autism clinic and a predictive model based on a stacked sparse autoencoder combined with a softmax classifier was constructed. We compared the proposed model with decision trees and support vector machines. Experiments show that the proposed model has a highest accuracy in predicting the severity of autism. Our method can help patients predict their condition and assist doctors in accurate diagnosis.

关键词： Autism severity stacked sparse autoencoder softmax classifier

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Applications of Neural Networks in Classifying Trained and Novel Gestures Using Surface Electromyography

Applications of Neural Networks in Classifying Trained and N...

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作者： Erik Lloyd University of Waterloo

学位级别：硕士

Current prosthetic control systems explored in the literature that use pattern recognition can perform a limited number of pre-assigned functions, as they must be trained using muscle signals for every movement the user wants to perform. The goal of this study was to explore the development of a prosthetic control system that can classify both trained and novel gestures, for applications in commercial prosthetic arms. The first objective of this study was to evaluate the feasibility of three different algorithms in classifying raw sEMG data for both trained isometric gestures, and for novel isometric gestures that were not included in the training data set. The algorithms used were; a feedforward multi-layer perceptron (FFMLP), a stacked sparse autoencoder (SSAE), and a convolution neural network (CNN). The second objective is to evaluate the algorithms' abilities to classify novel isometric gestures that were not included in the training data set, and to determine the effect of different gesture combinations on the classification accuracy. The third objective was to predict the binary (flexed/extended) digit positions without training the network using kinematic data from the participants hand. A g-tec USB Biosignal Amplifier was used to collect data from eight differential sEMG channels from 10 able-bodied participants. These participants performed 14 gestures including rest, that involved a variety of discrete finger flexion/extension tasks. Forty seconds of data were collected for each gesture at 1200 Hz from eight bipolar sEMG channels. These 14 gestures were then organized into 20 unique gesture combinations, where each combination consisted of a different sub-set of gestures used for training, and another sub-set used as the novel gestures, which were only used to test the algorithms' predictive capabilities. Participants were asked to perform the gestures in such a way where each digit was either fully flexed or fully extended to the best of their abilities.

关键词： myoelectric control surface electromyography deep learning prosthetic muti layer perceptron convolution neural network stacked sparse autoencoder

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Deep learning decision fusion for the classification of urban remote sensing data

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JOURNAL OF APPLIED REMOTE SENSING 2018年第1期12卷

作者： Abdi, Ghasem Samadzadegan, Farhad Reinartz, Peter Univ Tehran Fac Surveying & Geospatial Engn Coll Engn Tehran Iran German Aerosp Ctr DLR Remote Sensing Technol Inst Dept Photogrammetry & Image Anal Wessling Germany

Multisensor data fusion is one of the most common and popular remote sensing data classification topics by considering a robust and complete description about the objects of interest. Furthermore, deep feature extraction has recently attracted significant interest and has become a hot research topic in the geoscience and remote sensing research community. A deep learning decision fusion approach is presented to perform multisensor urban remote sensing data classification. After deep features are extracted by utilizing joint spectral-spatial information, a soft-decision made classifier is applied to train high-level feature representations and to fine-tune the deep learning framework. Next, a decision-level fusion classifies objects of interest by the joint use of sensors. Finally, a context-aware object-based postprocessing is used to enhance the classification results. A series of comparative experiments are conducted on the widely used dataset of 2014 IEEE GRSS data fusion contest. The obtained results illustrate the considerable advantages of the proposed deep learning decision fusion over the traditional classifiers. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： convolutional neural network decision-level fusion deep features deep learning stacked sparse autoencoder thermal hyperspectral

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Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework

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NEUROIMAGE-CLINICAL 2018年 18卷 290-297页

作者： He, Lili Li, Hailong Holland, Scott K. Yuan, Weihong Altaye, Mekibib Parikh, Nehal A. Cincinnati Childrens Hosp Med Ctr Dept Pediat Perinatal Inst Cincinnati OH 45229 USA Univ Cincinnati Coll Med Dept Pediat Cincinnati OH 45220 USA Cincinnati Childrens Hosp Med Ctr Pediat Neuroimaging Res Consortium Cincinnati OH 45229 USA Cincinnati Childrens Hosp Med Ctr Dept Radiol Cincinnati OH 45229 USA

Investigation of the brain's functional connectome can improve our understanding of how an individual brain's organizational changes influence cognitive function and could result in improved individual risk stratification. Brain connectome studies in adults and older children have shown that abnormal network properties may be useful as discriminative features and have exploited machine learning models for early diagnosis in a variety of neurological conditions. However, analogous studies in neonates are rare and with limited significant findings. In this paper, we propose an artificial neural network (ANN) framework for early prediction of cognitive deficits in very preterm infants based on functional connectome data from resting state fMRI. Specifically, we conducted feature selection via stacked sparse autoencoder and outcome prediction via support vector machine (SVM). The proposed ANN model was unsupervised learned using brain connectome data from 884 subjects in autism brain imaging data exchange database and SVM was cross-validated on 28 very preterm infants (born at 23-31 weeks of gestation and without brain injury;scanned at term-equivalent postmenstrual age). Using 90 regions of interests, we found that the ANN model applied to functional connectome data from very premature infants can predict cognitive outcome at 2 years of corrected age with an accuracy of 70.6% and area under receiver operating characteristic curve of 0.76. We also noted that several frontal lobe and somatosensory regions, significantly contributed to prediction of cognitive deficits 2 years later. Our work can be considered as a proof of concept for utilizing ANN models on functional connectome data to capture the individual variability inherent in the developing brains of preterm infants. The full potential of ANN will be realized and more robust conclusions drawn when applied to much larger neuroimaging datasets, as we plan to do.

关键词： Artificial neural network stacked sparse autoencoder Support vector machine Very preterm infants Functional MRI Cognitive deficit

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Automatic detection of translucency using a deep learning method from patches of clinical basal cell carcinoma images 10

Automatic detection of translucency using a deep learning me...

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10th Asia-Pacific-Signal-and-Information-Processing-Association Annual Summit and Conference (APSIPA ASC)

作者： Huang, He Kharazmi, Pegah McLean, David I. Lui, Harvey Wang, Z. Jane Lee, Tim K. Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada Univ British Columbia Dept Dermatol & Skin Sci Vancouver BC Canada BC Canc Agcy Canc Control Res Program Vancouver BC Canada

ISBN: (纸本)9789881476852

Translucency, defined as a jelly-like appearance, is a common clinical feature of basal cell carcinoma, the most common skin cancer. The feature plays an important role in diagnosing basal cell carcinoma in an early stage because the feature can be observed readily in clinical examinations with a high specificity of 93%. Therefore, translucency detection is a critical component of computer aided systems which aim at early detection of basal cell carcinoma. To address this problem, we proposed an automated method for analyzing patches of clinical basal cell carcinoma images using stacked sparse autoencoder ( SSAE). SSAE learns high-level features in unsupervised manner and all learned features are fed into a softmax classifier for translucency detection. Across the 4401 patches generated from 32 clinical images, the proposed method achieved a 93% detection accuracy from a five-fold cross-validation. The preliminary result suggested that the proposed method could detect translucency from skin images.

关键词： Translucency deep learning stacked sparse autoencoder basal cell carcinoma

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RS-MSSF Frame: Remote Sensing Image Classification Based on Extraction and Fusion of Multiple Spectral-Spatial Features 19th

RS-MSSF Frame: Remote Sensing Image Classification Based on ...

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19th Pacific-Rim Conference on Multimedia (PCM)

作者： Teffahi, Hanane Yao, Hongxun Harbin Inst Technol Sch Comp Sci & Technol Harbin Heilongjiang Peoples R China Algerian Space Agcy Algiers Algeria

ISBN: (纸本)9783030007676;9783030007669

Classifying remote sensing images with high spectral and spatial resolution became an important topic and challenging task in computer vision and remote sensing (RS) fields because of their huge dimensionality and computational complexity. Recently, many studies have already demonstrated the efficiency of employing spatial information where a combination of spectral and spatial information in a single classification framework have attracted special attention because of their capability to improve the classification accuracy. Shape and texture features are considered as two important types of spatial features in various applications of image processing. In this study, we extracted multiple features from spectral and spatial domains where we utilized texture and shape features, as well as spectral features, in order to obtain high classification accuracy. The spatial features considered in this study are produced by Gray Level Co-occurrence Matrix (GLCM) and Extended Multi-Attribute Profiles (EMAP), while, the extraction of deep spectral features is done by stacked sparse autoencoders. The obtained spectral-spatial features are concatenated directly as a simple feature fusion and are fed into the Support Vector Machine (SVM) classifier. We tested the proposed method on hyperspectral (HS) and multispectral (MS) images where the experiments demonstrated significantly the efficiency of the proposed framework in comparison with some recent spectral-spatial classification methods and with different classification frameworks based on the used extractors.

关键词： Remote sensing image classification Multiple features stacked sparse autoencoder Gray level co-occurrence matrix Extended multi-attribute profiles Support vector machine

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Computational Framework for Machine Fault Diagnosis with autoencoder Variants

Computational Framework for Machine Fault Diagnosis with Aut...

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International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)

作者： Verma, Nishchal K. Dixit, Sonal Sevakula, Rahul K. Salour, Al Indian Inst Technol Kanpur Dept Elect Engn Kanpur Uttar Pradesh India IBM India Cognit Comp Bengaluru India Boeing Co Boeing Res & Technol Seattle WA 29208 USA

ISBN: (纸本)9781538660577

Fault diagnosis is an integral component of Condition Monitoring systems used in industries. Deep learning techniques have recently been proven to be very effective in handling heterogeneous real time monitoring data. This paper presents a computational framework for machine fault diagnosis based on deep learning approaches and autoencoder variants. The framework includes methods for extraction of time domain and frequency domain features from raw data, training of autoencoders, and finally, detecting the presence of fault(s) using classifiers. A total of four different variants of autoencoders have been tested and analyzed for fault diagnosis framework;additionally, the option of reducing the learning complexity in autoencoder with layer wise feature selection strategy with ITER ranking has also been applied. For validation purpose, four standard fault diagnosis datasets were used. To obtain inferences, statistical tests was performed across all tested feature extraction techniques, autoencoder variants, and classifiers. The analysis suggests that stacked denoising sparse encoder in conjunction with frequency domain features and support vector classifiers gives consistent classification accuracy thus builds effective fault diagnosis system.

关键词： condition based monitoring deep learning feature extraction feature selection stacked sparse autoencoder denoising constraints

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