Deep Learning for COVID-19: COVID-19 Detection Based on Chest X-Ray Images by the Fusion of Deep Learning and Machine Learning Techniques

Pneumonia is a very common and fatal disease, which needs to be identified at the initial stages in order to prevent a patient having this disease from more damage and help him/her in saving his/her life. Various techniques are used for the diagnosis of pneumonia including chest X-ray, CT scan, blood culture, sputum culture, fluid sample, bronchoscopy, and pulse oximetry. Medical image analysis plays a vital role in the diagnosis of various diseases like MERS, COVID-19, pneumonia, etc. and is considered to be one of the auspicious research areas. To analyze chest X-ray images accurately, there is a need for an expert radiologist who possesses expertise and experience in the desired domain. According to the World Health Organization (WHO) report, about 2/3 people in the world still do not have access to the radiologist, in order to diagnose their disease. This study proposes a DL framework to diagnose pneumonia disease in an efficient and effective manner. Various Deep Convolutional Neural Network (DCNN) transfer learning techniques such as AlexNet, SqueezeNet, VGG16, VGG19, and Inception-V3 are utilized for extracting useful features from the chest X-ray images. In this study, several machine learning (ML) classifiers are utilized. The proposed system has been trained and tested on chest X-ray and CT images dataset. In order to examine the stability and effectiveness of the proposed system, different performance measures have been utilized. The proposed system is intended to be beneficial and supportive for medical doctors to accurately and efficiently diagnose pneumonia disease.

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Impact Analysis of Machine Learning Techniques for COVID-19 Diagnosis: A Critical Review

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i2.863 ◽

2021 ◽

Vol 12 (2) ◽

pp. 510-516

Author(s):

Anshul, Et. al.

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Severe Acute Respiratory Syndrome ◽

Impact Analysis ◽

Machine Learning Techniques ◽

X Ray ◽

Learning Techniques ◽

The World ◽

Almost All

COVID-19 virus belongs to the severe acute respiratory syndrome (SARS) family raised a situation of health emergency in almost all the countries of the world. Numerous machine learning and deep learning based techniques are used to diagnose COVID positive patients using different image modalities like CT SCAN, X-RAY, or CBX, etc. This paper provides the works done in COVID-19 diagnosis, the role of ML and DL based methods to solve this problem, and presents limitations with respect to COVID-19 diagnosis.

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A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-Rays and CT Scans Using Deep Learning and Machine Learning Methodologies

Diagnostics ◽

10.3390/diagnostics10080518 ◽

2020 ◽

Vol 10 (8) ◽

pp. 518 ◽

Cited By ~ 1

Author(s):

Hafsa Khalid ◽

Muzammil Hussain ◽

Mohammed A. Al Ghamdi ◽

Tayyaba Khalid ◽

Khadija Khalid ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Literature Review ◽

Ct Scan ◽

Systematic Literature Review ◽

Machine Learning Techniques ◽

Ct Scans ◽

X Rays ◽

X Ray ◽

Learning Techniques

The purpose of this research was to provide a “systematic literature review” of knee bone reports that are obtained by MRI, CT scans, and X-rays by using deep learning and machine learning techniques by comparing different approaches—to perform a comprehensive study on the deep learning and machine learning methodologies to diagnose knee bone diseases by detecting symptoms from X-ray, CT scan, and MRI images. This study will help those researchers who want to conduct research in the knee bone field. A comparative systematic literature review was conducted for the accomplishment of our work. A total of 32 papers were reviewed in this research. Six papers consist of X-rays of knee bone with deep learning methodologies, five papers cover the MRI of knee bone using deep learning approaches, and another five papers cover CT scans of knee bone with deep learning techniques. Another 16 papers cover the machine learning techniques for evaluating CT scans, X-rays, and MRIs of knee bone. This research compares the deep learning methodologies for CT scan, MRI, and X-ray reports on knee bone, comparing the accuracy of each technique, which can be used for future development. In the future, this research will be enhanced by comparing X-ray, CT-scan, and MRI reports of knee bone with information retrieval and big data techniques. The results show that deep learning techniques are best for X-ray, MRI, and CT scan images of the knee bone to diagnose diseases.

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Thoracic Diseases Prediction Algorithm From Chest X-Ray Images Using Machine Learning Techniques

SSRN Electronic Journal ◽

10.2139/ssrn.3350226 ◽

2019 ◽

Author(s):

Rushikesh Chavan ◽

Jidnasa Pillai ◽

Shravani Holkar ◽

Prajyot Salgaonkar ◽

Prakash Bhise

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Prediction Algorithm ◽

X Ray ◽

Thoracic Diseases ◽

Learning Techniques ◽

Chest X Ray

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Machine learning techniques as an eﬃcient alternative diagnostic tool for COVID-19 cases

10.22514/sv.2021.110 ◽

2021 ◽

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Sensitivity And Specificity ◽

Predictive Power ◽

Characteristic Curve ◽

False Negative ◽

Machine Learning Techniques ◽

Support Vector ◽

X Ray ◽

Learning Techniques

Background: The SARS-CoV-2 virus has demonstrated the weakness of many health systems worldwide, creating a saturation and lack of access to treatments. A bottleneck to fight this pandemic relates to the lack of diagnostic infrastructure for early detection of positive cases, particularly in rural and impoverished areas of developing countries. In this context, less costly and fast machine learning (ML) diagnosis-based systems are helpful. However, most of the research has focused on deep-learning techniques for diagnosis, which are computationally and technologically expensive. ML models have been mainly used as a benchmark and are not entirely explored in the existing literature on the topic of this paper. Objective: To analyze the capabilities of ML techniques (compared to deep learning) to diagnose COVID-19 cases based on X-ray images, assessing the performance of these techniques and using their predictive power for such a diagnosis. Methods: A factorial experiment was designed to establish this power with X-ray chest images of healthy, pneumonia, and COVID-19 infected patients. This design considers data-balancing methods, feature extraction approaches, different algorithms, and hyper-parameter optimization. The ML techniques were evaluated based on classification metrics, including accuracy, the area under the receiver operating characteristic curve (AUROC), F1-score, sensitivity, and specificity. Results: The design of experiment provided the mean and its confidence intervals for the predictive capability of different ML techniques, which reached AUROC values as high as 90% with suitable sensitivity and specificity. Among the learning algorithms, support vector machines and random forest performed best. The down-sampling method for unbalanced data improved the predictive power significantly for the images used in this study. Conclusions: Our investigation demonstrated that ML techniques are able to identify COVID-19 infected patients. The results provided suitable values of sensitivity and specificity, minimizing the false-positive or false-negative rates. The models were trained with significantly low computational resources, which helps to provide access and deployment in rural and impoverished areas.

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Multi-layer Hybrid Classification Model of COVID-19 Chest X-ray Images v1

10.17504/protocols.io.bzdzp276 ◽

2021 ◽

Author(s):

Thanakorn Poomkur ◽

Thakerng Wongsirichot

Keyword(s):

Machine Learning ◽

Classification Model ◽

Machine Learning Techniques ◽

Computational Time ◽

Support Vector ◽

X Ray ◽

Learning Techniques ◽

Hybrid Classification ◽

Chest X Ray ◽

Segmentation Image

The coronavirus disease of 2019 (COVID-19) has been declared a pandemic and has raised worldwide concern. Lung inflammation and respiratory failure are commonly observed in moderate-to-severe cases. Chest X-ray imaging is compulsory for diagnosis, and interpretation is commonly performed by skilled medical specialists. Many studies have been conducted using machine learning approaches such as Deep Learning (DL) with acceptable accuracy. However, other dimensions such as computational time were less discussed. Thus, our work is motivated to design anew computer-aided diagnosis (CADx) tool for identifying chest X-ray images of COVID-19 infection using machine learning techniques including Decision Tree (DT), Support Vector Machine (SVM), and Neural Networks (NNs). Our work is designed with the concept of multi-layer classification architecture and performs with minimal computational time and acceptable classification results. First, image segmentation, image enhancement and feature extraction techniques are performed. Second, machine learning techniques are selected based on classification performance. Finally, selected machine learning techniques are assembled into a multi-layer hybrid classification model for COVID-19 (MLHC-COVID-19). Specifically, the MLHC-COVID-19 consists of two layers, Layer I: Healthy and Unhealthy; Layer II: COVID-19 and non-COVID-19.

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A Novel Method to Identify Pneumonia through Analyzing Chest Radiographs Employing a Multichannel Convolutional Neural Network

Sensors ◽

10.3390/s20123482 ◽

2020 ◽

Vol 20 (12) ◽

pp. 3482

Author(s):

Abdullah-Al Nahid ◽

Niloy Sikder ◽

Anupam Kumar Bairagi ◽

Md. Abdur Razzaque ◽

Mehedi Masud ◽

...

Keyword(s):

Deep Learning ◽

Early Stage ◽

Machine Learning Techniques ◽

Past Century ◽

Radiographic Images ◽

X Ray ◽

The Past ◽

Learning Techniques ◽

Chest X Ray ◽

Pneumonia Diagnosis

Pneumonia is a virulent disease that causes the death of millions of people around the world. Every year it kills more children than malaria, AIDS, and measles combined and it accounts for approximately one in five child-deaths worldwide. The invention of antibiotics and vaccines in the past century has notably increased the survival rate of Pneumonia patients. Currently, the primary challenge is to detect the disease at an early stage and determine its type to initiate the appropriate treatment. Usually, a trained physician or a radiologist undertakes the task of diagnosing Pneumonia by examining the patient’s chest X-ray. However, the number of such trained individuals is nominal when compared to the 450 million people who get affected by Pneumonia every year. Fortunately, this challenge can be met by introducing modern computers and improved Machine Learning techniques in Pneumonia diagnosis. Researchers have been trying to develop a method to automatically detect Pneumonia using machines by analyzing and the symptoms of the disease and chest radiographic images of the patients for the past two decades. However, with the development of cogent Deep Learning algorithms, the formation of such an automatic system is very much within the realms of possibility. In this paper, a novel diagnostic method has been proposed while using Image Processing and Deep Learning techniques that are based on chest X-ray images to detect Pneumonia. The method has been tested on a widely used chest radiography dataset, and the obtained results indicate that the model is very much potent to be employed in an automatic Pneumonia diagnosis scheme.

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Implementation of Machine Learning Techniques for the Classification of Lung X-Ray Images Used to Detect COVID-19 in Humans

Iraqi Journal of Science ◽

10.24996/ijs.2021.62.6.35 ◽

2021 ◽

pp. 2099-2109

Author(s):

Maad M. Mijwil

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

Excellent Performance ◽

Wuhan City ◽

X Ray ◽

Learning Techniques ◽

Chest X Ray ◽

The Common

COVID-19 (Coronavirus disease-2019), commonly called Coronavirus or CoV, is a dangerous disease caused by the SARS-CoV-2 virus. It is one of the most widespread zoonotic diseases around the world, which started from one of the wet markets in Wuhan city. Its symptoms are similar to those of the common flu, including cough, fever, muscle pain, shortness of breath, and fatigue. This article suggests implementing machine learning techniques (Random Forest, Logistic Regression, Naïve Bayes, Support Vector Machine) by Python to classify a series of chest X-ray images that include viral pneumonia, COVID-19, and healthy (Not infected) cases in humans. The study includes more than 1400 images that are collected from the Kaggle platform. The experimental outcomes of this study confirmed that the supported vector machine technique has high accuracy and excellent performance in the classification of the disease, as reflected by values of 91.8% accuracy, 91.7% sensitivity, 95.9% specificity, 91.8% F1-score, and 97.6% AUC.

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Classification of Chest X-ray Images Using Machine Learning Techniques

10.20944/preprints202103.0408.v1 ◽

2021 ◽

Author(s):

Abdelmoty Ahmed ◽

Gamal Tharwat ◽

Belgacem Bouallegue ◽

Mahmoud Khattab ◽

Ahmad Al Moustafa ◽

...

Keyword(s):

Machine Learning ◽

Activation Function ◽

Machine Learning Techniques ◽

Mean Square ◽

Classification Result ◽

X Ray ◽

Number Of Layers ◽

Learning Techniques ◽

Chest X Ray

Machine Learning has completely transformed health care system, which transmits medical data through IOT sensors. So it is very important to encrypt them to protect patient data. encrypting medical images from a performance perspective consumes time; hence the use of an auto encoder is essential. An auto encoder is used in this work to compress the image as a vector prior to the encryption process. The digital image passes across description function and a decoder to get back the image in the proposed work; various experiments are carried out on hyper parameters to achieve the highest outcome of the classification. The findings demonstrate that the combination of Mean Square Logarithmic Error as the loss function, ADA grad as an optimizer, two layers for the encoder, and another reverse for the decoder, RELU as the activation function generates the best auto encoder results. The combination of Mean square error (lose function), RMS prop (optimizer), three layers for the encoder and another reverse for the decoder, and RELU (activation function) has the best classification result. All the experiments with different hyper parameter has run almost very close to each other even when changing the number of layers. The running time is between 9 and 16 second for each epoch.

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