scholarly journals Application Research on the Medical imaging testing of Novel Coronavirus Pneumonia  COVID-19 based on Transfer Learning

2021 ◽  
Author(s):  
shouqiang Liu ◽  
Mingyue Jiang ◽  
Liming Chen ◽  
Yang Wang
Keyword(s):  
Medical Imaging ◽  
Transfer Learning ◽  
Image Data ◽  
Training Model ◽  
Small Sample ◽  
Detection Methods ◽  
Nucleic Acid Detection ◽  
Small Data ◽  
Data Set ◽  
Novel Coronavirus

Abstract Novel coronavirus pneumonia (COVID-19) is a highly infectious and fatal pneumonia-type disease that poses a great threat to the public safety of society. A fast and efficient method for screening COVID19-positive patients is essential. At present, the main detection methods are nucleic acid detection of manual diagnosis and medical imaging (CT image/X-ray image), both of which take a long time to obtain the diagnosis result. This paper discusses the common processing methods for the problem of insufficient medical image data. Then, transfer learning and convolutional neural network were used to construct the screening and diagnosis model of COVID-19, and different migration models were analyzed and compared to select a better pre-training model, which was trained and analyzed under small data sets. Finally, it analyzes and discusses how to train a highly reliable model to quickly help doctors provide advice in the critical moment of epidemic prevention and control when only a small sample data set is available.

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

scholarly journals ESR white paper: blockchain and medical imaging

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
◽  
Elmar Kotter ◽  
Luis Marti-Bonmati ◽  
Adrian P. Brady ◽  
Nandita M. Desouza
Keyword(s):  
Medical Imaging ◽  
Image Data ◽  
Distributed Database ◽  
Relevant Information ◽  
White Paper ◽  
Imaging Data ◽  
Data Set ◽  
Blockchain Technology ◽  
Potential Applications ◽  
Basic Technology

AbstractBlockchain can be thought of as a distributed database allowing tracing of the origin of data, and who has manipulated a given data set in the past. Medical applications of blockchain technology are emerging. Blockchain has many potential applications in medical imaging, typically making use of the tracking of radiological or clinical data. Clinical applications of blockchain technology include the documentation of the contribution of different “authors” including AI algorithms to multipart reports, the documentation of the use of AI algorithms towards the diagnosis, the possibility to enhance the accessibility of relevant information in electronic medical records, and a better control of users over their personal health records. Applications of blockchain in research include a better traceability of image data within clinical trials, a better traceability of the contributions of image and annotation data for the training of AI algorithms, thus enhancing privacy and fairness, and potentially make imaging data for AI available in larger quantities. Blockchain also allows for dynamic consenting and has the potential to empower patients and giving them a better control who has accessed their health data. There are also many potential applications of blockchain technology for administrative purposes, like keeping track of learning achievements or the surveillance of medical devices. This article gives a brief introduction in the basic technology and terminology of blockchain technology and concentrates on the potential applications of blockchain in medical imaging.

scholarly journals Recognition of mango leaf disease using convolutional neural network models: a transfer learning approach

2021 ◽  
Vol 23 (3) ◽  
pp. 1681
Author(s):  
Aditya Rajbongshi ◽  
Thaharim Khan ◽  
Md. Mahbubur Rahman ◽  
Anik Pramanik ◽  
Shah Md Tanvir Siddiquee ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Image Data ◽  
Network Models ◽  
Plant Diseases ◽  
Prevention Measures ◽  
Neural Network Models ◽  
Data Set ◽  
Learning Techniques ◽  
Overall Performance ◽  
Mango Leaves

<p>The acknowledgment of plant diseases assumes an indispensable part in taking infectious prevention measures to improve the quality and amount of harvest yield. Mechanization of plant diseases is a lot advantageous as it decreases the checking work in an enormous cultivated area where mango is planted to a huge extend. Leaves being the food hotspot for plants, the early and precise recognition of leaf diseases is significant. This work focused on grouping and distinguishing the diseases of mango leaves through the process of CNN. DenseNet201, InceptionResNetV2, InceptionV3, ResNet50, ResNet152V2, and Xception all these models of CNN with transfer learning techniques are used here for getting better accuracy from the targeted data set. Image acquisition, image segmentation, and features extraction are the steps involved in disease detection. Different kinds of leaf diseases which are considered as the class for this work such as anthracnose, gall machi, powdery mildew, red rust are used in the dataset consisting of 1500 images of diseased and also healthy mango leaves image data another class is also added in the dataset. We have also evaluated the overall performance matrices and found that the DenseNet201 outperforms by obtaining the highest accuracy as 98.00% than other models.</p>

scholarly journals Automatic Benchmark Generation Framework for Malware Detection

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Guanghui Liang ◽  
Jianmin Pang ◽  
Zheng Shan ◽  
Runqing Yang ◽  
Yihang Chen
Keyword(s):  
Initial Data ◽  
Malware Detection ◽  
Detection Methods ◽  
Small Data ◽  
Security Threats ◽  
Improved Genetic Algorithm ◽  
Data Set ◽  
Detection Model ◽  
Manual Selection ◽  
Selection Of

To address emerging security threats, various malware detection methods have been proposed every year. Therefore, a small but representative set of malware samples are usually needed for detection model, especially for machine-learning-based malware detection models. However, current manual selection of representative samples from large unknown file collection is labor intensive and not scalable. In this paper, we firstly propose a framework that can automatically generate a small data set for malware detection. With this framework, we extract behavior features from a large initial data set and then use a hierarchical clustering technique to identify different types of malware. An improved genetic algorithm based on roulette wheel sampling is implemented to generate final test data set. The final data set is only one-eighteenth the volume of the initial data set, and evaluations show that the data set selected by the proposed framework is much smaller than the original one but does not lose nearly any semantics.

scholarly journals Ambulatory and Laboratory Stress Detection Based on Raw Electrocardiogram Signals Using a Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4408 ◽  
Author(s):  
Hyun-Myung Cho ◽  
Heesu Park ◽  
Suh-Yeon Dong ◽  
Inchan Youn
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Mental Arithmetic ◽  
Small Data ◽  
Data Sets ◽  
Learning Method ◽  
Data Set ◽  
Electrocardiogram Signals ◽  
Proposed Model ◽  
Small Data Set

The goals of this study are the suggestion of a better classification method for detecting stressed states based on raw electrocardiogram (ECG) data and a method for training a deep neural network (DNN) with a smaller data set. We suggest an end-to-end architecture to detect stress using raw ECGs. The architecture consists of successive stages that contain convolutional layers. In this study, two kinds of data sets are used to train and validate the model: A driving data set and a mental arithmetic data set, which smaller than the driving data set. We apply a transfer learning method to train a model with a small data set. The proposed model shows better performance, based on receiver operating curves, than conventional methods. Compared with other DNN methods using raw ECGs, the proposed model improves the accuracy from 87.39% to 90.19%. The transfer learning method improves accuracy by 12.01% and 10.06% when 10 s and 60 s of ECG signals, respectively, are used in the model. In conclusion, our model outperforms previous models using raw ECGs from a small data set and, so, we believe that our model can significantly contribute to mobile healthcare for stress management in daily life.

scholarly journals Learning-Based Detection for Malicious Android Application Using Code Vectorization

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lin Liu ◽  
Wang Ren ◽  
Feng Xie ◽  
Shengwei Yi ◽  
Junkai Yi ◽  
...  
Keyword(s):  
Hybrid Model ◽  
Malware Detection ◽  
Virus Detection ◽  
Training Model ◽  
Malicious Code ◽  
Detection Methods ◽  
Android Application ◽  
Fusion Model ◽  
Data Set ◽  
Android Applications

The malicious APK (Android Application Package) makers use some techniques such as code obfuscation and code encryption to avoid existing detection methods, which poses new challenges for accurate virus detection and makes it more and more difficult to detect the malicious code. A report indicates that a new malicious app for Android is created every 10 seconds. To combat this serious malware activity, a scalable malware detection approach is needed, which can effectively and efficiently identify the malware apps. Common static detection methods often rely on Hash matching and analysis of viruses, which cannot quickly detect new malicious Android applications and their variants. In this paper, a malicious Android application detection method is proposed, which is implemented by the deep network fusion model. The hybrid model only needs to use the sample training model to achieve high accuracy in the identification of the malicious applications, which is more suitable for the detection of the new malicious Android applications than the existing methods. This method extracts the static features in the core code of the Android application by decompiling APK files, then performs code vectorization processing, and uses the deep learning network for classification and discrimination. Our experiments with a data set containing 10,170 apps show that the decisions from the hybrid model can increase the malware detection rate significantly on a real device, which verifies the superiority of this method in the detection of malicious codes.

scholarly journals The Classification Between Prostate Transitional Zone Cancer and Hyperplasia Using Deep Transfer Learning from Disease-Related Images: A Retrospective Study

2020 ◽  
Author(s):  
Bo Hu ◽  
Lin-Feng Yan ◽  
Yang Yang ◽  
Ying-Zhi Sun ◽  
Cui Yue ◽  
...  
Keyword(s):  
Sample Size ◽  
Transfer Learning ◽  
Small Sample Size ◽  
Transitional Zone ◽  
Small Sample ◽  
Fine Tuning ◽  
Small Data ◽  
Deep Convolutional Neural Networks ◽  
Tuning Method ◽  
Apparent Diffusion

Abstract Background The diagnosis of prostate transition zone cancers (PTZC) remains a clinical challenge due to its similarity to benign prostatic hyperplasia (BPH) on MRI. The Deep Convolutional Neural Networks showed high efficacy in medical imaging but was limited by the small data size. A transfer learning method was combined with deep learning to overcome this challenge.Methods A retrospective investigation was conducted on 217 patients enrolled from our hospital database (208 patients) and The Cancer Imaging Archive (9 patients). Based on the T2 weighted images (T2WIs) and apparent diffusion coefficient (ADC) maps of these patients, DCNN models were trained and compared between different TL database (ImageNet vs. disease-related images) and protocols (from scratch, fine-tuning or transductive transferring).Results PTZC and BPH can be classified through traditional DCNN. The efficacy of transfer learning from ImageNet was limited but improved by transferring knowledge from the disease-related images. Furthermore, transductive transfer learning from disease-related images had the comparable efficacies with the fine-tuning method. Limitations include retrospective design and relatively small sample size.Conclusion For PTZC with a small sample size, the accurate diagnosis can be achieved via the deep transfer learning from disease-related images.

A novel deep convolution generative adversarial transfer learning model for data-driven assembly quality diagnosis

2021 ◽  
pp. 1-15
Author(s):  
Wentao Luo ◽  
Pingfa Feng ◽  
Jianfu Zhang ◽  
Dingwen Yu ◽  
Zhijun Wu
Keyword(s):  
Transfer Learning ◽  
Feature Learning ◽  
Learning Model ◽  
Small Sample ◽  
Data Driven ◽  
Training Dataset ◽  
Small Data ◽  
Assembly Quality ◽  
Learning Module ◽  
Quality Diagnosis

As the service life of the assembly equipment are short, the tightening data it produces are very limited. Therefore, data-driven assembly quality diagnosis is still a challenge task in industries. Transfer learning can be used to address small data problems. However, transfer learning has strict requirements on the training dataset, which is hard to satisfy. To solve the above problem, an Improved Deep Convolution Generative Adversarial Transfer Learning Model (IDCGAN-TM) is proposed, which integrates three modules: The generative learning module automatically produces source datasets based on small target datasets by using the improved generative-adversarial theory. The feature learning module improves the feature extraction ability by building a lightweight deep learning model (DL). The transfer learning module consists of a pre-trained DL and a one fully connected layer to better perform the intelligent quality diagnosis on the training small sample data. A parallel computing method is adopted to obtain produced source data efficiently. Real assembly quality diagnosis cases are designed and discussed to validate the advance of the proposed model. In addition, the comparison experiments are designed to show that the proposed approach holds the better transfer diagnosis performance compared with the existing three state-of-art approaches.

scholarly journals Evaluation of Image Forgery Detection Using Multi-Scale Weber Local Descriptors

2015 ◽  
Vol 24 (04) ◽  
pp. 1540016 ◽  
Author(s):  
Muhammad Hussain ◽  
Sahar Qasem ◽  
George Bebis ◽  
Ghulam Muhammad ◽  
Hatim Aboalsamh ◽  
...  
Keyword(s):  
Digital Image ◽  
Image Data ◽  
Feature Space ◽  
Detection Methods ◽  
Support Vector ◽  
Data Sets ◽  
Forgery Detection ◽  
Data Set ◽  
Multi Scale ◽  
Copy Move Forgery Detection

Due to the maturing of digital image processing techniques, there are many tools that can forge an image easily without leaving visible traces and lead to the problem of the authentication of digital images. Based on the assumption that forgery alters the texture micro-patterns in a digital image and texture descriptors can be used for modeling this change; we employed two stat-of-the-art local texture descriptors: multi-scale Weber's law descriptor (multi-WLD) and multi-scale local binary pattern (multi-LBP) for splicing and copy-move forgery detection. As the tamper traces are not visible to open eyes, so the chrominance components of an image encode these traces and were used for modeling tamper traces with the texture descriptors. To reduce the dimension of the feature space and get rid of redundant features, we employed locally learning based (LLB) algorithm. For identifying an image as authentic or tampered, Support vector machine (SVM) was used. This paper presents the thorough investigation for the validation of this forgery detection method. The experiments were conducted on three benchmark image data sets, namely, CASIA v1.0, CASIA v2.0, and Columbia color. The experimental results showed that the accuracy rate of multi-WLD based method was 94.19% on CASIA v1.0, 96.52% on CASIA v2.0, and 94.17% on Columbia data set. It is not only significantly better than multi-LBP based method, but also it outperforms other stat-of-the-art similar forgery detection methods.

FINGERPRINT SILICON REPLICAS: STATIC AND DYNAMIC FEATURES FOR VITALITY DETECTION USING AN OPTICAL CAPTURE DEVICE

2008 ◽  
Vol 08 (04) ◽  
pp. 495-512 ◽  
Author(s):  
PIETRO COLI ◽  
GIAN LUCA MARCIALIS ◽  
FABIO ROLI
Keyword(s):  
Performance Improvement ◽  
Small Sample Size ◽  
Small Sample ◽  
Small Data ◽  
Data Sets ◽  
Dynamic Features ◽  
Data Set ◽  
Feature Sets ◽  
Small Data Sets ◽  
Verification Systems

The automatic vitality detection of a fingerprint has become an important issue in personal verification systems based on this biometric. It has been shown that fake fingerprints made using materials like gelatine or silicon can deceive commonly used sensors. Recently, the extraction of vitality features from fingerprint images has been proposed to address this problem. Among others, static and dynamic features have been separately studied so far, thus their respective merits are not yet clear; especially because reported results were often obtained with different sensors and using small data sets which could have obscured relative merits, due to the potential small sample-size issues. In this paper, we compare some static and dynamic features by experiments on a larger data set and using the same optical sensor for the extraction of both feature sets. We dealt with fingerprint stamps made using liquid silicon rubber. Reported results show the relative merits of static and dynamic features and the performance improvement achievable by using such features together.

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