Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging

Tuberculosis is a potential fatal disease with high morbidity and mortality rates. Tuberculosis death rates are rising, posing a serious health threat in several poor countries around the world. To address this issue, we proposed a novel method for detecting tuberculosis in chest X-ray (CXR) images that uses a three-phased approach to distinguish tuberculosis such as segmentation, feature extraction, and classification. In a CXR, we utilized the Weiner filter to distinguish and reduce the impulse noise. The features were extracted from CXR images and trained using a decision tree classifier known as the stacked loopy decision tree (SLDT) classifier. For the classification process, the ROI-based morphological approach was applied in the mentioned three-phased approach, and the feature extraction was accomplished through chromatic and Prewitt-edge highlights.

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A Review on Deep Learning Approaches for COVID-19 Detection in Chest X-Ray Images

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.38692 ◽

2021 ◽

Vol 9 (10) ◽

pp. 1792-1801

Author(s):

Tanishka Dodiya

Keyword(s):

Deep Learning ◽

Detection System ◽

High Rate ◽

Learning Approaches ◽

X Ray ◽

First Case ◽

X Ray Imaging ◽

The World ◽

Chest X Ray ◽

Diagnostic Capabilities

Abstract: COVID-19 also famously known as Coronavirus is one of the deadliest viruses found in the world, which has a high rate in both demise and spread. This has caused a severe pandemic in the world. The virus was first reported in Wuhan, China, registering causes like pneumonia. The first case was encountered on December 31, 2019. As of 20th October 2021, more than 242 million cases have been reported in more than 188 countries, and it has around 5 million deaths. COVID- 19 infected persons have pneumonia-like symptoms, and the infection damages the body's respiratory organs, making breathing difficult. The elemental clinical equipment as of now being employed for the analysis of COVID-19 is RT-PCR, which is costly, touchy, and requires specific clinical workforce. According to recent studies, chest X-ray scans include important information about the start of the infection, and this information may be examined so that diagnosis and treatment can begin sooner. This is where artificial intelligence meets the diagnostic capabilities of intimate clinicians. X-ray imaging is an effectively available apparatus that can be an astounding option in the COVID-19 diagnosis. The architecture usually used are VGG16, ResNet50, DenseNet121, Xception, ResNet18, etc. This deep learning based COVID detection system can be installed in hospitals for early diagnosis, or it can be used as a second opinion. Keywords: COVID-19, Deep Learning, CNN, CT-Image, Transfer Learning, VGG, ResNet, DenseNet

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DEEP LEARNING (CNN) MODEL FOR COVID-19 DETECTION FROM CHEST XRAY IMAGES

International Journal of Engineering Applied Sciences and Technology ◽

10.33564/ijeast.2021.v06i04.010 ◽

2021 ◽

Vol 6 (4) ◽

Author(s):

Ahmed Mohamed ◽

Ahmed Abdelhady

Keyword(s):

Neural Network ◽

Deep Learning ◽

Early Stage ◽

Open Data ◽

Disease Outbreak ◽

Imaging Features ◽

X Ray ◽

X Ray Imaging ◽

Chest X Ray ◽

Data Source

The Coronavirus disease outbreak result in many people to have severe respira- tory problems and it was recognized as a global health threat. Since the virus is targeting the lungs in the human body initially, chest x-ray imaging features were considered to be useful for the detection of the infection in the early stage. In this study, the chest x-ray data of 130 infected patients from an open data source that referenced Cohen J. Morrison P. Dao L., 2020 was used to build a CNN( Convolutional Neural-Network) model for the early detection of the disease. The model was trained with both infected and not-infected peoples’ chest x-ray images with 100 epochs which led to 0.98 accuracy finally. In order to use this model as a professional diagnosis element, it is highly recommended it be improved with more images and the model can be restructured to get a better accuracy.

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Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images

Sensors ◽

10.3390/s21196655 ◽

2021 ◽

Vol 21 (19) ◽

pp. 6655

Author(s):

Michael Horry ◽

Subrata Chakraborty ◽

Biswajeet Pradhan ◽

Manoranjan Paul ◽

Douglas Gomes ◽

...

Keyword(s):

Lung Cancer ◽

Computer Vision ◽

Deep Learning ◽

Decision Tree ◽

Decision Trees ◽

Lung Nodules ◽

X Rays ◽

Learning Models ◽

X Ray ◽

Chest X Ray

Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the “black-box” nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.

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Automatic Detection of COVID-19 and Pneumonia from Chest X-Ray using Transfer Learning

10.1101/2020.05.27.20100297 ◽

2020 ◽

Author(s):

Sarath Pathari ◽

Rahul U

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Clinical Picture ◽

Bacterial Pneumonia ◽

Automatic Detection ◽

Neural System ◽

Cutting Edge ◽

X Ray ◽

X Ray Imaging ◽

Chest X Ray

In this study, a dataset of X-ray images from patients with common viral pneumonia, bacterial pneumonia, confirmed Covid-19 disease was utilized for the automatic detection of the Coronavirus disease. The point of the investigation is to assess the exhibition of cutting edge convolutional neural system structures proposed over the ongoing years for clinical picture order. In particular, the system called Transfer Learning was received. With transfer learning, the location of different variations from the norm in little clinical picture datasets is a reachable objective, regularly yielding amazing outcomes. The datasets used in this trial. Firstly, a collection of 24000 X-ray images includes 6000 images for confirmed Covid-19 disease,6000 confirmed common bacterial pneumonia and 6000 images of normal conditions. The information was gathered and expanded from the accessible X-Ray pictures on open clinical stores. The outcomes recommend that Deep Learning with X-Ray imaging may separate noteworthy biomarkers identified with the Covid-19 sickness, while the best precision, affectability, and particularity acquired is 97.83%, 96.81%, and 98.56% individually.

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

ELKOMIKA Jurnal Teknik Energi Elektrik Teknik Telekomunikasi & Teknik Elektronika ◽

10.26760/elkomika.v8i2.443 ◽

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

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Transfer learning to detect COVID-19 automatically from X-ray images, using convolutional neural networks

10.1101/2020.08.25.20182170 ◽

2020 ◽

Author(s):

Mundher Taresh ◽

Ningbo Zhu ◽

Talal Ahmed Ali Ali

Keyword(s):

Deep Learning ◽

Confusion Matrix ◽

High Accuracy ◽

Classification Model ◽

Statistical Parameters ◽

Data Set ◽

X Ray ◽

X Ray Imaging ◽

Chest X Ray ◽

Novel Coronavirus

AbstractNovel coronavirus pneumonia (COVID-19) is a contagious disease that has already caused thousands of deaths and infected millions of people worldwide. Thus, all technological gadgets that allow the fast detection of COVID-19 infection with high accuracy can offer help to healthcare professionals. This study is purposed to explore the effectiveness of artificial intelligence (AI) in the rapid and reliable detection of COVID-19 based on chest X-ray imaging. In this study, reliable pre-trained deep learning algorithms were applied to achieve the automatic detection of COVID-19-induced pneumonia from digital chest X-ray images.Moreover, the study aims to evaluate the performance of advanced neural architectures proposed for the classification of medical images over recent years. The data set used in the experiments involves 274 COVID-19 cases, 380 viral pneumonia, and 380 healthy cases, which was collected from the available X-ray images on public medical repositories. The confusion matrix provided a basis for testing the post-classification model. Furthermore, an open-source library PyCM* was used to support the statistical parameters. The study revealed the superiority of Model VGG16 over other models applied to conduct this research where the model performed best in terms of overall scores and based-class scores. According to the research results, deep learning with X-ray imaging is useful in the collection of critical biological markers associated with COVID-19 infection. The technique is conducive for the physicians to make a diagnosis of COVID-19 infection. Meanwhile, the high accuracy of this computer-aided diagnostic tool can significantly improve the speed and accuracy of COVID-19 diagnosis.

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An Online Automatic Corona Diagnose System Based on Chest X-ray Images

10.21203/rs.3.rs-64472/v1 ◽

2020 ◽

Author(s):

Reza Amini Gougeh

Keyword(s):

Deep Learning ◽

Ct Scan ◽

Disease Patient ◽

Small Cities ◽

X Ray ◽

Online System ◽

Patient Status ◽

X Ray Imaging ◽

Therapeutic Guidelines ◽

Chest X Ray

Abstract An outbreak of SARS-CoV-2 shocked healthcare systems around the world. It began in December 2019 in Wuhan, China, and spread out in over 120 countries in less than three months. Imaging technologies helped in COVID-19 fast and reliable diagnosis. CT-Scan and X-ray imaging are popular methods. This study is focused on X-ray imaging, concerning limitations in small cities to access CT-Scan and its costs. Using deep learning models helps to diagnose precisely and quickly. We aimed to design an online system based on deep learning, which reports lung engagement with the disease, patient status, and therapeutic guidelines. Our objective was to relieve pressure on radiologists and minimize the interval between imaging and diagnosing. VGG19, VGG16, InceptionV3, and ResNet50 were evaluated to be considered as the main code of the online diagnosing system. VGG16, with 98.92% accuracy, achieved the best score. VGG19 performed quite similarly to VGG16. VGG19, InceptionV3 and ResNet50 obtained 98.90, 71.79 and 28.27 subsequently.

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DenseCapsNet: Detection of COVID-19 X-ray Images Using a Capsule Network

10.21203/rs.3.rs-84220/v1 ◽

2020 ◽

Author(s):

Hao Quan ◽

Xiaosong Xu ◽

Tingting Zheng ◽

Zhi Li ◽

Mingfang Zhao ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Data Augmentation ◽

Detection Performance ◽

Convolution Neural Network ◽

X Ray ◽

Learning Framework ◽

X Ray Imaging ◽

Chest X Ray ◽

Novel Coronavirus

Abstract Objective: A deep learning framework for detecting COVID-19 is developed, and a small amount of chest X-ray data is used to accurately screen COVID-19.Methods: In this paper, we propose a deep learning framework that integrates convolution neural network and capsule network. DenseNet and CapsNet fusion are used to give full play to their respective advantages, reduce the dependence of convolution neural network on a large amount of data, and can quickly and accurately distinguish COVID-19 from Non-COVID-19 through chest X-ray imaging.Results: A total of 1472 chest X-ray COVID-19 and non-COVID-19 images are used, this method can achieve an accuracy of 99.32% and a precision of 100%, with 98.55% sensitivity and 100% specificity.Conclusion: These results show that the deep fusion neural network DenseCapsNet has good performance in novel coronavirus pneumonia X-ray detection. We also prove through experiments that the detection performance of DenseCapsNet is not affected fundamentally by a lack of data augmentation and pre-training.

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Chest Diseases Detection based on Deep Learning

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35941 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 4142-4146

Author(s):

Puneet Gupta

Keyword(s):

Deep Learning ◽

Human Body ◽

Medical Image ◽

Screening Tool ◽

X Ray ◽

Maximum Accuracy ◽

X Ray Imaging ◽

Different Types ◽

Chest X Ray ◽

Treat All

The paper is on our project that is all about detecting 14 different types of chest diseases using x-ray images of chest. It also neglects the apparels and jewelry’s present on human body while performing the x-ray test thus giving us maximum accuracy of diseases detection. The key goal of project is to know the percentage of diseases detection of all the 14 different tests performed on human chest with maximum accuracy. Chest x-ray imaging is a vital screening and medicine tool for many life threating diseases, however because of shortage of radiologists, the screening tool cannot be wont to treat all patients. Deep learning based mostly medical image classifiers are one potential answer. This project runs, uploads, method and generates reports at any given purpose of your time with accuracy.

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