scholarly journals Deep learning for COVID-19 diagnosis based on chest X-ray images

2021 ◽  
Vol 11 (5) ◽  
pp. 4531
Author(s):  
Nashat Alrefai ◽  
Othman Ibrahim
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Promising Result ◽  
Test Accuracy ◽  
Training Time ◽  
X Ray ◽  
Global Pandemic ◽  
Augmentation Techniques ◽  
Chest X Ray ◽  
Using Data

Coronavirus disease 2019 (COVID-19) is a recent global pandemic that has affected many countries around the world, causing serious health problems, especially in the lungs. Although temperature testing is suggested as a firstline test for COVID-19, it was not reliable because many diseases have the same symptoms. Thus, we propose a deep learning method based on X-ray images that used a convolutional neural network (CNN) and transfer learning (TL) for COVID-19 diagnosis, and using gradient-weighted class activation mapping (Grad-CAM) technique for producing visual explanations for the COVID-19 infection area in the lung. The low sample size of coronavirus samples was considered a challenge, thus, this issue was overridden using data augmentation techniques. The study found that the proposed (CNN) and the modified pre-trained networks VGG16 and InceptionV3 achieved a promising result for COVID-19 diagnosis by using chest X-ray images. The proposed CNN was able to differentiate 284 patients with COVID-19 or normal with 98.2 percent for training accuracy and 96.66 percent for test accuracy and 100.0 percent sensitivity. The modified VGG16 achieved the best classification result between all with 100.0 percent for training accuracy and 98.33 percent for test accuracy and 100.0 percent sensitivity, but the proposed CNN overcame the others in the side of reducing the computational complexity and training time significantly.

scholarly journals Pneumonia Classification using Deep Learning in Healthcare

2020 ◽  
Vol 9 (4) ◽  
pp. 1715-1723 ◽  
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Research Work ◽  
Neural System ◽  
X Ray ◽  
Python Language ◽  
Learning Techniques ◽  
Improved Outcomes ◽  
Augmentation Techniques ◽  
Chest X Ray

There is a great growing interest in the domain of deep learning techniques for identifying and classifying images with various datasets. An enormous availability of datasets (e.g. ChestX-Ray14 dataset) has developed a keen interest in deep learning. Pneumonia is a disease that is caused by various bacteria, virus etc. X-ray is one of the major diagnosis tools for diagnosing pneumonia. This research work mainly proposes a convolutional neural system (CNN) model prepared without any preparation to group and identify the occurrence of pneumonia disease from a given assortment of chest X-ray image tests. Dissimilar to different strategies that depend exclusively on more learning draws near or conventional carefully assembled systems to accomplish an amazing grouping execution, and developed a convolutional neural arrange model without any preparation to separate and character the images to decide whether an individual is suffering with pneumonia. This model could help alleviate the dependability and difficult challenges frequently confronted to manage therapeutic problems. In this paper, CNN algorithm has been used along with different data augmentation techniques for improving the classification accuracies which has been discussed to increase the performance which will help in improving the validation and training accuracies and characterization of exactness of the CNN model and accomplished various results. This experiment was carried out using python language and has shown improved outcomes.

scholarly journals MFDNN: Multi-channel feature deep neural network algorithm to identify Covid19 chest X-ray images

2021 ◽  
Author(s):  
Liangrui Pan ◽  
boya ji ◽  
Xiaoqi wang ◽  
shaoliang peng
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Test Accuracy ◽  
Learning Networks ◽  
X Ray ◽  
Network Algorithm ◽  
Life Saving ◽  
Chest X Ray ◽  
Neural Network Algorithm

The use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID-19) is life-saving important for both patients and doctors. This research proposed a multi-channel feature deep neural network algorithm to screen people infected with COVID-19. The algorithm integrates data oversampling technology and a multi-channel feature deep neural network model to carry out the training process in an end-to-end manner. In the experiment, we used a publicly available CXI database with 10,192 Normal, 6012 Lung Opacity (Non-COVID lung infection), and 1345 Viral Pneumonia images. Compared with traditional deep learning models (Densenet201, ResNet50, VGG19, GoogLeNet), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Secondly, compared with the latest CoroDet model, the MFDNN algorithm is 1.91% higher than the CoroDet model in the experiment of detecting the four categories of COVID19 infected persons. Finally, our experimental code will be placed at https://github.com/panliangrui/covid19.

scholarly journals An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Okeke Stephen ◽  
Mangal Sain ◽  
Uchenna Joseph Maduh ◽  
Do-Un Jeong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Data Augmentation ◽  
Classification Performance ◽  
Learning Approaches ◽  
X Ray ◽  
Chest X Ray

This study proposes a convolutional neural network model trained from scratch to classify and detect the presence of pneumonia from a collection of chest X-ray image samples. Unlike other methods that rely solely on transfer learning approaches or traditional handcrafted techniques to achieve a remarkable classification performance, we constructed a convolutional neural network model from scratch to extract features from a given chest X-ray image and classify it to determine if a person is infected with pneumonia. This model could help mitigate the reliability and interpretability challenges often faced when dealing with medical imagery. Unlike other deep learning classification tasks with sufficient image repository, it is difficult to obtain a large amount of pneumonia dataset for this classification task; therefore, we deployed several data augmentation algorithms to improve the validation and classification accuracy of the CNN model and achieved remarkable validation accuracy.

scholarly journals A Deep Learning Approach to Detect COVID-19 Patients from Chest X-ray Images

AI ◽  
2020 ◽  
Vol 1 (3) ◽  
pp. 418-435
Author(s):  
Khandaker Haque ◽  
Ahmed Abdelgawad
Keyword(s):  
Deep Learning ◽  
World Health ◽  
X Rays ◽  
X Ray ◽  
Global Pandemic ◽  
Proposed Model ◽  
Dental Diseases ◽  
Chest X Ray ◽  
Artery Disease ◽  
Health Organization

Deep Learning has improved multi-fold in recent years and it has been playing a great role in image classification which also includes medical imaging. Convolutional Neural Networks (CNNs) have been performing well in detecting many diseases including coronary artery disease, malaria, Alzheimer’s disease, different dental diseases, and Parkinson’s disease. Like other cases, CNN has a substantial prospect in detecting COVID-19 patients with medical images like chest X-rays and CTs. Coronavirus or COVID-19 has been declared a global pandemic by the World Health Organization (WHO). As of 8 August 2020, the total COVID-19 confirmed cases are 19.18 M and deaths are 0.716 M worldwide. Detecting Coronavirus positive patients is very important in preventing the spread of this virus. On this conquest, a CNN model is proposed to detect COVID-19 patients from chest X-ray images. Two more CNN models with different number of convolution layers and three other models based on pretrained ResNet50, VGG-16 and VGG-19 are evaluated with comparative analytical analysis. All six models are trained and validated with Dataset 1 and Dataset 2. Dataset 1 has 201 normal and 201 COVID-19 chest X-rays whereas Dataset 2 is comparatively larger with 659 normal and 295 COVID-19 chest X-ray images. The proposed model performs with an accuracy of 98.3% and a precision of 96.72% with Dataset 2. This model gives the Receiver Operating Characteristic (ROC) curve area of 0.983 and F1-score of 98.3 with Dataset 2. Moreover, this work shows a comparative analysis of how change in convolutional layers and increase in dataset affect classifying performances.

scholarly journals Deteksi Penyakit Covid-19 Berdasarkan Citra X-Ray Menggunakan Deep Residual Network

2020 ◽  
Vol 8 (2) ◽  
pp. 443
Author(s):  
YULI SUN HARIYANI ◽  
SUGONDO HADIYOSO ◽  
THOMHERT SUPRAPTO SIADARI
Keyword(s):  
Deep Learning ◽  
Clinical Diagnosis ◽  
Learning Approach ◽  
Residual Network ◽  
X Ray ◽  
Global Pandemic ◽  
X Ray Imaging ◽  
Clinical Diagnoses ◽  
Chest X Ray ◽  
Supporting Tool

ABSTRAKPenyakit Coronavirus-2019 atau Covid-19 telah menjadi pandemi global dan menjadi masalah utama yang harus segera dikendalikan. Salah satu cara yang dapat dilakukan adalah memutus rantai penyebaran virus tersebut dengan melakukan deteksi dan melalukan karantina. Pencitraan X-Ray dapat dijadikan alternatif dalam mempelajari Covid-19. X-Ray dianggap mampu menggambarkan kondisi paru-paru pada pasien Covid-19 dan dapat menjadi alat bantu diagnosa klinis. Pada penelitian ini, kami mengusulkan pendekatan deep learning berbasis residual deep network untuk deteksi Covid-19 melalui citra chest X-Ray. Evaluasi yang dilakukan untuk mengetahui performa metode yang diusulkan berupa precision, recall, F1, dan accuracy. Hasil eksperimen menunjukkan bahwa usulan metode ini memberikan precision, recall, F1 dan accuracy masing-masing 0,98, 0,95, 0,97 dan 99%. Pada masa mendatang, studi ini diharapkan dapat divalidasi dan kemudian digunakan untuk melengkapi diagnosa klinis oleh dokter.Kata kunci: Coronavirus-2019, Covid-19, chest X-Ray, deep learning, residual network ABSTRACTCoronavirus-2019 or Covid-19 disease has become a global pandemic and is a major problem that must be stopped immediately. One of the ways that can be done to stop its spreading is to break the spreading chain of the virus by detecting and doing quarantine. X-Ray imaging can be used as an alternative in detecting Covid-19. X-Ray is considered able to describe the condition of the lungs for Covid-19 suspected patients and can be a supporting tool for clinical diagnosis. In this study, we propose a residual based deep learning approach for Covid-19 detection using chest X-Ray images. Evaluation is carried out to determine the performance of the proposed method in the form of precision, recall, F1 and accuracy. Experiments results show that our proposed method provides precision, recall, F1 and accuracy respectively 0.98, 0.95, 0.97 and 99%. In the future, this study is expected to be validated and then used to support clinical diagnoses by doctors.Keywords: Coronavirus-2019, Covid-19, chest X-Ray, deep learning, residual network

scholarly journals Review on Deep Learning Based Automatic Detection of COVID-19 from Chest-X Ray Images

2021 ◽  
pp. 10-14
Author(s):  
Debaraj Rana ◽  
Swarna Prabha Jena ◽  
Subrat Kumar Pradhan
Keyword(s):  
Deep Learning ◽  
Virus Disease ◽  
Health Sector ◽  
Cost Effective ◽  
Data Set ◽  
X Ray ◽  
Time Consumption ◽  
Corona Virus ◽  
Global Pandemic ◽  
Chest X Ray

The Global pandemic declared Corona Virus Disease (COVID 19) has affected severely tothe health of human being over the globe. More than 15 crore around worldwide have been affected by the Novel Corona virus and it is progressing rapidly. Mainly in the health sector, the hospitals are not properly equipped with proper diagnosis system which can detect the disease accurately with less time consumption. The Chest X Ray image are taking less time and cost effective which can be used for detection of COVID 19 even the severity can also be determine form the CXR images. In the current research many researchers are focusing on implementation of Deep learning method for accurate and quick detection of COVID 19 which can help the radiologist for evaluation of the disease. In this review, proposed Deep learning methodology from the literature have been discussed with their experimental data set. This review could help to develop modified architecture which gives more improvement in the diagnosis in term of computational complexity and time consumption

scholarly journals Evaluation of Deep Convolutional Generative Adversarial Networks for Data Augmentation of Chest X-ray Images

2020 ◽  
Vol 13 (1) ◽  
pp. 8
Author(s):  
Sagar Kora Venu ◽  
Sridhar Ravula
Keyword(s):  
Data Augmentation ◽  
Medical Condition ◽  
Training Data ◽  
Superior Performance ◽  
Generative Adversarial Networks ◽  
X Rays ◽  
Generative Adversarial Network ◽  
X Ray ◽  
Augmentation Techniques ◽  
Chest X Ray

Medical image datasets are usually imbalanced due to the high costs of obtaining the data and time-consuming annotations. Training a deep neural network model on such datasets to accurately classify the medical condition does not yield the desired results as they often over-fit the majority class samples’ data. Data augmentation is often performed on the training data to address the issue by position augmentation techniques such as scaling, cropping, flipping, padding, rotation, translation, affine transformation, and color augmentation techniques such as brightness, contrast, saturation, and hue to increase the dataset sizes. Radiologists generally use chest X-rays for the diagnosis of pneumonia. Due to patient privacy concerns, access to such data is often protected. In this study, we performed data augmentation on the Chest X-ray dataset to generate artificial chest X-ray images of the under-represented class through generative modeling techniques such as the Deep Convolutional Generative Adversarial Network (DCGAN). With just 1341 chest X-ray images labeled as Normal, artificial samples were created by retaining similar characteristics to the original data with this technique. Evaluating the model resulted in a Fréchet Distance of Inception (FID) score of 1.289. We further show the superior performance of a CNN classifier trained on the DCGAN augmented dataset.

scholarly journals Diagnosing heart failure from chest X-ray images using deep learning

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
T Matsumoto ◽  
S Kodera ◽  
H Shinohara ◽  
A Kiyosue ◽  
Y Higashikuni ◽  
...  
Keyword(s):  
Heart Failure ◽  
Deep Learning ◽  
Data Augmentation ◽  
Learning Algorithm ◽  
Funding Source ◽  
X Rays ◽  
X Ray ◽  
Cardiothoracic Ratio ◽  
Prediction Probability ◽  
Chest X Ray

Abstract   The development of deep learning technology has enabled machines to achieve high-level accuracy in interpreting medical images. While many previous studies have examined the detection of pulmonary nodules and cardiomegaly in chest X-rays using deep learning, the application of this technology to heart failure remains rare. In this study, we investigated the performance of a deep learning algorithm in terms of diagnosing heart failure using images obtained from chest X-rays. We used 952 chest X-ray images from a labeled database published by the National Institutes of Health. Two cardiologists respectively verified and relabeled these images, for a total of 260 “normal” and 378 “heart failure” images, and the remainder were discarded because they had been incorrectly labeled. In this study “heart failure” was defined as “cardiomegaly or congestion”, in a chest X-ray with cardiothoracic ratio (CTR) over 50% or radiographic presence of pulmonary edema. To enable the machine to extract a sufficient number of features from the images, we used the general machine learning approach called data augmentation and transfer learning. Owing mostly to this technique and the adequate relabeling process, we established a model to detect heart failure in chest X-ray by applying deep learning, and obtained an accuracy of 82%. Sensitivity and specificity to heart failure were 75% and 94.4%, respectively. Furthermore, heatmap imaging allowed us to visualize decisions made by the machine. The figure shows randomly selected examples of the prediction probabilities and heatmaps of the chest X-rays from the dataset. The original image is on the left and its heatmap is on the right, with its prediction probability written below. The red areas on the heatmaps show important regions, according to which the machine determined the classification. While some images with ambiguous radiolucency such as (e) and (f) were prone to be misdiagnosed by this model, most of the images like (a)–(d) were diagnosed correctly. Deep learning can thus help support the diagnosis of heart failure using chest X-ray images. Heatmaps and probabilities of prediction Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): JSPS KAKENHI

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