An efficient deep belief network for Detection of Coronavirus Disease COVID-19

2020 ◽  
pp. 05-13
Author(s):  
Shaymaa Adnan Abdulrahma ◽  
◽  
◽  
Abdel-Badeeh M. Salem
Keyword(s):  
Deep Learning ◽  
Early Detection ◽  
Respiratory Diseases ◽  
Medical Images ◽  
Respiratory Viruses ◽  
Data Repository ◽  
Data Set ◽  
Belief Network ◽  
Learning Technique ◽  
Normal Lungs

COVID-19 infection is one of the most dangerous respiratory viruses and the early detection of this disease reduces the speed of its spread among people . The goal of this virus is to infect the lung by creating white patchy shadows inside the lungs. This paper presents an intelligent method based on the deep learning technique to analyze the medical images of respiratory diseases . Two data set was used in this experiment first dataset is normal lungs taken from Kaggle data repository. While abnormal lungs taken from (https://github.com/muhammedtalo/COVID-19) .The results show that the proposed system identifies the COVID-19 cases with an accuracy of 90%.

scholarly journals A novel augmented deep transfer learning for classification of COVID-19 and other thoracic diseases from X-rays

2021 ◽  
Author(s):  
Fouzia Altaf ◽  
Syed M. S. Islam ◽  
Naeem Khalid Janjua
Keyword(s):  
Deep Learning ◽  
Medical Imaging ◽  
Transfer Learning ◽  
Medical Images ◽  
Training Data ◽  
Target Domain ◽  
Data Set ◽  
Learning Technique ◽  
Multi Class Classification ◽  
Effective Transfer

AbstractDeep learning has provided numerous breakthroughs in natural imaging tasks. However, its successful application to medical images is severely handicapped with the limited amount of annotated training data. Transfer learning is commonly adopted for the medical imaging tasks. However, a large covariant shift between the source domain of natural images and target domain of medical images results in poor transfer learning. Moreover, scarcity of annotated data for the medical imaging tasks causes further problems for effective transfer learning. To address these problems, we develop an augmented ensemble transfer learning technique that leads to significant performance gain over the conventional transfer learning. Our technique uses an ensemble of deep learning models, where the architecture of each network is modified with extra layers to account for dimensionality change between the images of source and target data domains. Moreover, the model is hierarchically tuned to the target domain with augmented training data. Along with the network ensemble, we also utilize an ensemble of dictionaries that are based on features extracted from the augmented models. The dictionary ensemble provides an additional performance boost to our method. We first establish the effectiveness of our technique with the challenging ChestXray-14 radiography data set. Our experimental results show more than 50% reduction in the error rate with our method as compared to the baseline transfer learning technique. We then apply our technique to a recent COVID-19 data set for binary and multi-class classification tasks. Our technique achieves 99.49% accuracy for the binary classification, and 99.24% for multi-class classification.

scholarly journals Deep Convolution Neural Networks for Medical Image Analysis

2018 ◽  
Vol 7 (3.33) ◽  
pp. 115 ◽  
Author(s):  
Myung Jae Lim ◽  
Da Eun Kim ◽  
Dong Kun Chung ◽  
Hoon Lim ◽  
Young Man Kwon
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Early Detection ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Histopathological Image ◽  
Approach Method

Breast cancer is a highly contagious disease that has killed many people all over the world. It can be fully recovered from early detection. To enable the early detection of the breast cancer, it is very important to classify accurately whether it is breast cancer or not. Recently, the deep learning approach method on the medical images such as these histopathologic images of the breast cancer is showing higher level of accuracy and efficiency compared to the conventional methods. In this paper, the breast cancer histopathological image that is difficult to be distinguished was analyzed visually. And among the deep learning algorithms, the CNN(Convolutional Neural Network) specialized for the image was used to perform comparative analysis on whether it is breast cancer or not. Among the CNN algorithms, VGG16 and InceptionV3 were used, and transfer learning was used for the effective application of these algorithms.The data used in this paper is breast cancer histopathological image dataset classifying the benign and malignant of BreakHis. In the 2-class classification task, InceptionV3 achieved 98% accuracy. It is expected that this deep learning approach method will support the development of disease diagnosis through medical images.  

scholarly journals A Novel Method of Hyperspectral Data Classification Based on Transfer Learning and Deep Belief Network

2019 ◽  
Vol 9 (7) ◽  
pp. 1379 ◽  
Author(s):  
Ke Li ◽  
Mingju Wang ◽  
Yixin Liu ◽  
Nan Yu ◽  
Wei Lan
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Spatial Information ◽  
Hyperspectral Data ◽  
Deep Belief Network ◽  
Learning Method ◽  
Target Domain ◽  
Data Set ◽  
Belief Network ◽  
Target Data

The classification of hyperspectral data using deep learning methods can obtain better results than the previous shallow classifiers, but deep learning algorithms have some limitations. These algorithms require a large amount of data to train the network, while also needing a certain amount of labeled data to fine-tune the network. In this paper, we propose a new hyperspectral data processing method based on transfer learning and the deep learning method. First, we use a hyperspectral data set that is similar to the target data set to pre-train the deep learning network. Then, we use the transfer learning method to find the common features of the source domain data and target domain data. Second, we propose a model structure that combines the deep transfer learning model to utilize a combination of spatial information and spectral information. Using transfer learning, we can obtain the spectral features. Then, we obtain several principal components of the target data. These will be regarded as the spatial features of the target domain data, and we use the joint features for the classifier. The data are obtained from a hyperspectral public database. Using the same amount of data, our method based on transfer learning and deep belief network obtains better classification accuracy in a shorter amount of time.

scholarly journals epiLiver a novel tumor specific, high throughput and cost-effective blood test for specific detection of liver cancer (HCC)

2021 ◽  
Author(s):  
David Cheishvili ◽  
Chifat Wong ◽  
Mohammad Karim ◽  
Mohammad Kibria ◽  
Nusrat Jahan ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Early Detection ◽  
High Throughput ◽  
Cost Effective ◽  
Gene Expression Omnibus ◽  
Morbidity And Mortality ◽  
Data Repository ◽  
Liquid Biopsies ◽  
Data Set ◽  
Normal Tissues

Abstract Robust cost effective and high-throughput tests for early detection of cancer in otherwise healthy people could potentially revolutionize public-health and the heavy personal and public burden of the morbidity and mortality from cancer. Several studies have delineated tumor specific DNA methylation profiles that could serve as biomarkers for early detection of Hepatocellular Carcinoma (HCC) as well as other cancers in liquid biopsies. Several published DNA methylation markers fail to distinguish HCC DNA from DNA from other tissues and other cancers that are potentially present in plasma. We describe a set of DNA methylation signatures in HCC that are “categorically” distinct from normal tissues and blood DNA methylation profiles. We develop a classifier combined of 4 CG sites that is sufficient to detect HCC in TCGA HCC data set at high accuracy. A single CG site at the F12 gene is sufficient to differentiate HCC samples from thousands of other blood samples, normal tissues and 31 tumors in the TCGA and Gene Expression Omnibus (GEO) data repository (n = 11,704). A “next generation sequencing”-targeted-multiplexed high-throughput assay was developed, which was used to examine in a clinical study plasma samples from HCC, chronic hepatitis B (CHB) patients and healthy controls (n = 398). The sensitivity for HCC detection was 84.5% at a specificity of 95% and AUC of 0.94. Applying this assay for routine follow up of people who are at high risk for developing HCC could have a significant impact on reducing the morbidity and mortality from HCC.

scholarly journals Automatic Detection of COVID-19 Infection from Chest X-ray using Deep Learning

2020 ◽  
Author(s):  
kishore Medhi ◽  
Md. Jamil ◽  
Iftekhar Hussain
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Respiratory System ◽  
Data Repository ◽  
X Ray ◽  
Network Method ◽  
Chest X Ray

COVID-19 infection has created a panic across the globe in recent times. Early detection of COVID-19 infection can save many lives in the pre-vailing situation. This virus affects the respiratory system of a person and creates white patchy shadows in the lungs. Deep learning is one of the most effective Artificial Intelligence techniques to analyse chest X-ray images for efficient and reliable COVID-19 screening. In this paper, we have proposed a Deep Convolutional Neural Network method for fast and dependable identification of COVID-19 infection cases from the patient chest X-ray images. To validate the performance of the proposed system, chest X-ray images of more than 150 confirmed COVID-19 patients from the Kaggle data repository are used in the experimentation. The results show that the proposed system identifies the cases with an accuracy of 93%.

scholarly journals epiLiver a novel tumor specific, high throughput and cost-effective blood test for specific detection of liver cancer (HCC)

2021 ◽  
Author(s):  
David Cheishvili ◽  
Chifat Wong ◽  
Mohammad Mahbubul Karim ◽  
Mohammad Golam Kibria ◽  
Nusrat Jahan ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Early Detection ◽  
High Throughput ◽  
Cost Effective ◽  
Gene Expression Omnibus ◽  
Morbidity And Mortality ◽  
Data Repository ◽  
Liquid Biopsies ◽  
Data Set ◽  
Normal Tissues

AbstractRobust cost effective and high-throughput tests for early detection of cancer in otherwise healthy people could potentially revolutionize public-health and the heavy personal and public burden of the morbidity and mortality from cancer. Several studies have delineated tumor specific DNA methylation profiles that could serve as biomarkers for early detection of Hepatocellular Carcinoma (HCC) as well as other cancers in liquid biopsies. Several published DNA methylation markers fail to distinguish HCC DNA from DNA from other tissues and other cancers that are potentially present in plasma. We describe a set of DNA methylation signatures in HCC that are “categorically” distinct from normal tissues and blood DNA methylation profiles. We develop a classifier combined of 4 CG sites that is sufficient to detect HCC in TCGA HCC data set at high accuracy. A single CG site at the F12 gene is sufficient to differentiate HCC samples from thousands of other blood samples, normal tissues and 31 tumors in the TCGA and Gene Expression Omnibus (GEO) data repository (n=11,704). A “next generation sequencing”-targeted-multiplexed high-throughput assay was developed, which was used to examine in a clinical study plasma samples from HCC, chronic hepatitis B (CHB) patients and healthy controls (n=398). The sensitivity for HCC detection was 84.5% at a specificity of 95% and AUC of 0.94. Applying this assay for routine follow up of people who are at high risk for developing HCC could have a significant impact on reducing the morbidity and mortality from HCC.

Deep Learning Approaches for Whiteboard Image Quality Enhancement

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Data ◽  
Quality Enhancement ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Image Quality Enhancement ◽  
Processing Techniques ◽  
White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

Sign in / Sign up

Export Citation Format

Share Document