Image Classification Using Transfer Learning and Deep Learning

Deep learning models have demonstrated improved efficacy in image classification since the ImageNet Large Scale Visual Recognition Challenge started since 2010. Classification of images has further augmented in the field of computer vision with the dawn of transfer learning. To train a model on huge dataset demands huge computational resources and add a lot of cost to learning. Transfer learning allows to reduce on cost of learning and also help avoid reinventing the wheel. There are several pretrained models like VGG16, VGG19, ResNet50, Inceptionv3, EfficientNet etc which are widely used. This paper demonstrates image classification using pretrained deep neural network model VGG16 which is trained on images from ImageNet dataset. After obtaining the convolutional base model, a new deep neural network model is built on top of it for image classification based on fully connected network. This classifier will use features extracted from the convolutional base model.

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RISC-V Virtual Platform-Based Convolutional Neural Network Accelerator Implemented in SystemC

Electronics ◽

10.3390/electronics10131514 ◽

2021 ◽

Vol 10 (13) ◽

pp. 1514

Author(s):

Seung-Ho Lim ◽

WoonSik William Suh ◽

Jin-Young Kim ◽

Sang-Young Cho

Keyword(s):

Neural Network ◽

Deep Learning ◽

Network Model ◽

Neural Network Model ◽

Deep Neural Network ◽

System Level ◽

Neural Network Models ◽

Data Set ◽

Embedded Device ◽

Virtual Platform

The optimization for hardware processor and system for performing deep learning operations such as Convolutional Neural Networks (CNN) in resource limited embedded devices are recent active research area. In order to perform an optimized deep neural network model using the limited computational unit and memory of an embedded device, it is necessary to quickly apply various configurations of hardware modules to various deep neural network models and find the optimal combination. The Electronic System Level (ESL) Simulator based on SystemC is very useful for rapid hardware modeling and verification. In this paper, we designed and implemented a Deep Learning Accelerator (DLA) that performs Deep Neural Network (DNN) operation based on the RISC-V Virtual Platform implemented in SystemC in order to enable rapid and diverse analysis of deep learning operations in an embedded device based on the RISC-V processor, which is a recently emerging embedded processor. The developed RISC-V based DLA prototype can analyze the hardware requirements according to the CNN data set through the configuration of the CNN DLA architecture, and it is possible to run RISC-V compiled software on the platform, can perform a real neural network model like Darknet. We performed the Darknet CNN model on the developed DLA prototype, and confirmed that computational overhead and inference errors can be analyzed with the DLA prototype developed by analyzing the DLA architecture for various data sets.

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Comparisons of different deep learning-based methods on fault diagnosis for geared system

International Journal of Distributed Sensor Networks ◽

10.1177/1550147719888169 ◽

2019 ◽

Vol 15 (11) ◽

pp. 155014771988816 ◽

Cited By ~ 2

Author(s):

Bing Han ◽

Xiaohui Yang ◽

Yafeng Ren ◽

Wanggui Lan

Keyword(s):

Neural Network ◽

Deep Learning ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Mechanical System ◽

Network Model ◽

Learning Theory ◽

Neural Network Model ◽

Deep Neural Network ◽

Gear Fault

The running state of a geared transmission system affects the stability and reliability of the whole mechanical system. It will greatly reduce the maintenance cost of a mechanical system to identify the faulty state of the geared transmission system. Based on the measured gear fault vibration signals and the deep learning theory, four fault diagnosis neural network models including fast Fourier transform–deep belief network model, wavelet transform–convolutional neural network model, Hilbert-Huang transform–convolutional neural network model, and comprehensive deep neural network model are developed and trained respectively. The results show that the gear fault diagnosis method based on deep learning theory can effectively identify various gear faults under real test conditions. The comprehensive deep neural network model is the most effective one in gear fault recognition.

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Deep learning-based inertia tensor identification of the combined spacecraft

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410020904555 ◽

2020 ◽

Vol 234 (7) ◽

pp. 1356-1366

Author(s):

Weimeng Chu ◽

Shunan Wu ◽

Xiao He ◽

Yufei Liu ◽

Zhigang Wu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Network Model ◽

Neural Network Model ◽

Deep Neural Network ◽

Angular Rate ◽

Inertia Tensor ◽

Measurement Noise ◽

Identification Performance ◽

Operating Environments

The identification accuracy of inertia tensor of combined spacecraft, which is composed by a servicing spacecraft and a captured target, could be easily affected by the measurement noise of angular rate. Due to frequently changing operating environments of combined spacecraft in space, the measurement noise of angular rate can be very complex. In this paper, an inertia tensor identification approach based on deep learning method is proposed to improve the ability of identifying inertia tensor of combined spacecraft in the presence of complex measurement noise. A deep neural network model for identification is constructed and trained by enough training data and a designed learning strategy. To verify the identification performance of the proposed deep neural network model, two testing set with different ranks of measure noises are used for simulation tests. Comparison tests are also delivered among the proposed deep neural network model, recursive least squares identification method, and tradition deep neural network model. The comparison results show that the proposed deep neural network model yields a more accurate and stable identification performance for inertia tensor of combined spacecraft in changeable and complex operating environments.

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Tongue fissure visualization by using deep learning – an example of the application of artificial intelligence in traditional medicine

10.21203/rs.2.19210/v2 ◽

2020 ◽

Author(s):

Wen-Hsien Chang ◽

Han-Kuei Wu ◽

Lun-chien Lo ◽

William W. L. Hsiao ◽

Hsueh-Ting Chu ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Transfer Learning ◽

Network Model ◽

Neural Network Model ◽

Graphics Processing Units ◽

Network Models ◽

Neural Network Models ◽

The Neural Network ◽

Objective Evidence

Abstract Background: Traditional Chinese medicine (TCM) describes physiological and pathological changes inside and outside the human body by the application of four methods of diagnosis. One of the four methods, tongue diagnosis, is widely used by TCM physicians, since it allows direct observations that prevent discrepancies in the patient’s history and, as such, provides clinically important, objective evidence. The clinical significance of tongue features has been explored in both TCM and modern medicine. However, TCM physicians may have different interpretations of the features displayed by the same tongue, and therefore intra- and inter-observer agreements are relatively low. If an automated interpretation system could be developed, more consistent results could be obtained, and learning could also be more efficient. This study will apply a recently developed deep learning method to the classification of tongue features, and indicate the regions where the features are located.Methods: A large number of tongue photographs with labeled fissures were used. Transfer learning was conducted using the ImageNet-pretrained ResNet50 model to determine whether tongue fissures were identified on a tongue photograph. Often, the neural network model lacks interpretability, and users cannot understand how the model determines the presence of tongue fissures. Therefore, Gradient-weighted Class Activation Mapping (Grad-CAM) was also applied to directly mark the tongue features on the tongue image. Results: Only 6 epochs were trained in this study and no graphics processing units (GPUs) were used. It took less than 4 minutes for each epoch to be trained. The correct rate for the test set was approximately 70%. After the model training was completed, Grad-CAM was applied to localize tongue fissures in each image. The neural network model not only determined whether tongue fissures existed, but also allowed users to learn about the tongue fissure regions.Conclusions: This study demonstrated how to apply transfer learning using the ImageNet-pretrained ResNet50 model for the identification and localization of tongue fissures and regions. The neural network model built in this study provided interpretability and intuitiveness, (often lacking in general neural network models), and improved the feasibility for clinical application.

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Filtering Reordering Table Using a Novel Recursive Autoencoder Model for Statistical Machine Translation

Mathematical Problems in Engineering ◽

10.1155/2017/3492587 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9

Author(s):

Jinying Kong ◽

Yating Yang ◽

Lei Wang ◽

Xi Zhou ◽

Tonghai Jiang ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Machine Translation ◽

Network Model ◽

Neural Network Model ◽

Deep Neural Network ◽

Statistical Machine Translation ◽

Learning Problem ◽

Proposed Model ◽

Discriminative Model

In phrase-based machine translation (PBMT) systems, the reordering table and phrase table are very large and redundant. Unlike most previous works which aim to filter phrase table, this paper proposes a novel deep neural network model to prune reordering table. We cast the task as a deep learning problem where we jointly train two models: a generative model to implement rule embedding and a discriminative model to classify rules. The main contribution of this paper is that we optimize the reordering model in PBMT by filtering reordering table using a recursive autoencoder model. To evaluate the performance of the proposed model, we performed it on public corpus to measure its reordering ability. The experimental results show that our approach obtains high improvement in BLEU score with less scale of reordering table on two language pairs: English-Chinese (+0.28) and Uyghur-Chinese (+0.33) MT.

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Abstract 12569: Analysis of Deep Learning Models for Prediction of Heart Failure Mortality

Circulation ◽

10.1161/circ.142.suppl_3.12569 ◽

2020 ◽

Vol 142 (Suppl_3) ◽

Author(s):

Chayakrit Krittanawong ◽

Kipp W Johnson ◽

Usman Baber ◽

Mehmet Aydar ◽

Zhen Wang ◽

...

Keyword(s):

Neural Network ◽

Heart Failure ◽

Deep Learning ◽

Network Model ◽

Neural Network Model ◽

Deep Neural Network ◽

Validation Cohort ◽

Sensitivity Analyses ◽

Brier Score ◽

Learning Models

Introduction: Heart failure (HF) is a leading cause of hospitalization, morbidity and mortality. Deep learning (DL) techniques appear to show promising results in risk stratification and prognosis in several conditions in medicine. However, few methods using DL exist to help quantitatively estimate prognosis of HF. We hypothesized that deep learning (DL) techniques could prognosis of HF using simple variables. We propose application of a custom-built deep-neural-network model to identify mortality in HF patients. Methods: Custom-built deep-neural-networks were assessed using survey data from 42,147 participants from the National Health and Nutrition Examination Survey 1999-2016 (NHANES). Variables were selected using clinical judgment and stepwise backward regressions to develop prediction models. We partitioned the data into training and testing sets and repetitive experiments. We then evaluated model performance based on discrimination and calibration including the area under the receiver-operator characteristics curve (C-statistics), balanced accuracy, probability calibration with sigmoid, and the Brier score, respectively. As sensitivity analyses, we examined results limited to cases with complete clinical information available. We validated models’ performance using Mount Sinai database. Results: Of 42,147 participants with 4,060 variables, 1,491 (3.5%) had HF and HF mortality was 51.8%. In validation cohort, of 26,333 HF patients, the mortality in HF patients was 405 (1.5%). Final model using only 20 variables (age, race, gender, BMI, smoking, alcohol consumption, HTN, COPD, SBP, DBP, HR, HDL, LDL, CRP, A1C, BUN, creatinine, hemoglobin, sodium level, on statin) was tested. A state-of-the-art deep learning models achieved high accuracy for predicting mortality in HF patients with an AUC of 0.96 (95% CI: 0.95-0.99) in the first cohort and AUC of 0.93 (95% CI: 0.91-0.96) in validation cohort. Conclusions: A deep neural network model has shown to have high predictive accuracy and discriminative and calibrative power for prediction of HF mortality. Further research can delineate the clinical implications of DL in predicting HF mortality.

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Research on Fish Image Classification Based on Transfer Learning and Convolutional Neural Network Model

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) ◽

10.1109/fskd.2018.8686892 ◽

2018 ◽

Author(s):

Yaxin Ma ◽

Pengfei Zhang ◽

Yunhan Tang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Image Classification ◽

Transfer Learning ◽

Network Model ◽

Neural Network Model

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Tongue fissure visualization by using deep learning – an example of the application of artificial intelligence in traditional medicine

10.21203/rs.2.19210/v3 ◽

2020 ◽

Author(s):

Wen-Hsien Chang ◽

Han-Kuei Wu ◽

Lun-chien Lo ◽

William W. L. Hsiao ◽

Hsueh-Ting Chu ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Transfer Learning ◽

Network Model ◽

Neural Network Model ◽

Graphics Processing Units ◽

Network Models ◽

Neural Network Models ◽

The Neural Network ◽

Objective Evidence

Abstract Background Traditional Chinese medicine (TCM) describes physiological and pathological changes inside and outside the human body by the application of four methods of diagnosis. One of the four methods, tongue diagnosis, is widely used by TCM physicians, since it allows direct observations that prevent discrepancies in the patient’s history and, as such, provides clinically important, objective evidence. The clinical significance of tongue features has been explored in both TCM and modern medicine. However, TCM physicians may have different interpretations of the features displayed by the same tongue, and therefore intra- and inter-observer agreements are relatively low. If an automated interpretation system could be developed, more consistent results could be obtained, and learning could also be more efficient. This study will apply a recently developed deep learning method to the classification of tongue features, and indicate the regions where the features are located. Methods A large number of tongue photographs with labeled fissures were used. Transfer learning was conducted using the ImageNet-pretrained ResNet50 model to determine whether tongue fissures were identified on a tongue photograph. Often, the neural network model lacks interpretability, and users cannot understand how the model determines the presence of tongue fissures. Therefore, Gradient-weighted Class Activation Mapping (Grad-CAM) was also applied to directly mark the tongue features on the tongue image. Results Only 6 epochs were trained in this study and no graphics processing units (GPUs) were used. It took less than 4 minutes for each epoch to be trained. The correct rate for the test set was approximately 70%. After the model training was completed, Grad-CAM was applied to localize tongue fissures in each image. The neural network model not only determined whether tongue fissures existed, but also allowed users to learn about the tongue fissure regions. Conclusions This study demonstrated how to apply transfer learning using the ImageNet-pretrained ResNet50 model for the identification and localization of tongue fissures and regions. The neural network model built in this study provided interpretability and intuitiveness, (often lacking in general neural network models), and improved the feasibility for clinical application.

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