scholarly journals Employing Texture Features of Chest X-Ray Images and Machine Learning in COVID-19 Detection and Classification

MENDEL ◽  
2021 ◽  
Vol 27 (1) ◽  
pp. 9-17
Author(s):  
Hiam Alquran ◽  
Mohammad Alsleti ◽  
Roaa Alsharif ◽  
Isam Abu Qasmieh ◽  
Ali Mohammad Alqudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Texture Features ◽  
Automated System ◽  
Rt Pcr ◽  
False Negatives ◽  
X Ray ◽  
Organ Systems ◽  
Wide Range ◽  
Chest X Ray

The novel coronavirus (nCoV-19) was first detected in December 2019. It had spread worldwide and was declared coronavirus disease (COVID-19) pandemic by March 2020. Patients presented with a wide range of symptoms affecting multiple organ systems predominantly the lungs. Severe cases required intensive care unit (ICU) admissions while there were asymptomatic cases as well. Although early detection of the COVID-19 virus by Real-time reverse transcription-polymerase chain reaction (RT-PCR) is effective, it is not efficient; as there can be false negatives, it is time consuming and expensive. To increase the accuracy of in-vivo detection, radiological image-based methods like a simple chest X-ray (CXR) can be utilized. This reduces the false negatives as compared to solely using the RT-PCR technique. This paper employs various image processing techniques besides extracted texture features from the radiological images and feeds them to different artificial intelligence (AI) scenarios to distinguish between normal, pneumonia, and COVID-19 cases. The best scenario is then adopted to build an automated system that can segment the chest region from the acquired image, enhance the segmented region then extract the texture features, and finally, classify it into one of the three classes. The best overall accuracy achieved is 93.1% by exploiting Ensemble classifier. Utilizing radiological data to conform to a machine learning format reduces the detection time and increase the chances of survival.

scholarly journals ANALYZING CHEST X-RAY TO DIFFERENTIATE SYMPTOMS OF COVID-19 THROUGH ML APPROACH

2021 ◽  
Vol 5 (12) ◽  
Author(s):  
Deepali R Deshpande ◽  
Raj L Shah ◽  
Anish N Shaha
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Time Constraints ◽  
X Rays ◽  
Rt Pcr ◽  
X Ray ◽  
Social Distancing ◽  
Machine Learning Model ◽  
Chest X Ray

The motive behind the project is to build a machine learning model for detection of Covid-19. Using this model, it is possible to classify images of chest x-rays into normal patients, pneumatic patients, and covid-19 positive patients. This CNN based model will help drastically to save time constraints among the patients. Instead of relying on limited RT-PCR kits, just a simple chest x-ray can help us determine health of the patient. Not only we get immediate results, but we can also practice social distancing norms more effectively.

scholarly journals An approach to categorize chest X-ray images using sparse categorical cross entropy

2021 ◽  
Vol 24 (3) ◽  
pp. 1700
Author(s):  
Chaithanya B. N. ◽  
Swasthika Jain T. J. ◽  
A. Usha Ruby ◽  
Ayesha Parveen
Keyword(s):  
Machine Learning ◽  
Cost Effective ◽  
Cross Entropy ◽  
Ct Scans ◽  
Rt Pcr ◽  
Time Saving ◽  
X Ray ◽  
Proposed Model ◽  
Chest X Ray ◽  
Entropy Technique

The Coronavirus disease (COVID-19) pandemic is the most recent threat to global health. Reverse transcription-polymerase chain reaction (RT-PCR) testing, computed tomography (CT) scans, and chest X-ray (CXR) images are being used to identify Coronavirus, one of the most serious community viruses of the twenty-first century. Because CT scans and RT-PCR analyses are not available in most health divisions, CXR images are typically the most time-saving and cost-effective tool for physicians in making decisions. Artificial intelligence and machine learning have become increasingly popular because of recent technical advancements. The goal of this project is to combine machine learning, deep learning, and the health-care sector to create a categorization technique for detecting the Coronavirus and other respiratory disorders. The three conditions evaluated in this study were COVID-19, viral Pneumonia, and normal lungs. Using X-ray pictures, this research developed a sparse categorical cross-entropy technique for recognizing all three categories. The proposed model had a training accuracy of 91% and a training loss of 0.63, as well as a validation accuracy of 81% and a validation loss of 0.7108.

scholarly journals Application of Machine Learning in Diagnosis of COVID-19 Through X-Ray and CT Images: A Scoping Review

2021 ◽  
Vol 8 ◽  
Author(s):  
Hossein Mohammad-Rahimi ◽  
Mohadeseh Nadimi ◽  
Azadeh Ghalyanchi-Langeroudi ◽  
Mohammad Taheri ◽  
Soudeh Ghafouri-Fard
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Chest Ct ◽  
Accurate Diagnosis ◽  
Ct Scans ◽  
Rt Pcr ◽  
Learning Methods ◽  
X Ray ◽  
Machine Learning Methods ◽  
Chest X Ray

Coronavirus disease, first detected in late 2019 (COVID-19), has spread fast throughout the world, leading to high mortality. This condition can be diagnosed using RT-PCR technique on nasopharyngeal and throat swabs with sensitivity values ranging from 30 to 70%. However, chest CT scans and X-ray images have been reported to have sensitivity values of 98 and 69%, respectively. The application of machine learning methods on CT and X-ray images has facilitated the accurate diagnosis of COVID-19. In this study, we reviewed studies which used machine and deep learning methods on chest X-ray images and CT scans for COVID-19 diagnosis and compared their performance. The accuracy of these methods ranged from 76% to more than 99%, indicating the applicability of machine and deep learning methods in the clinical diagnosis of COVID-19.

scholarly journals An Integrated Framework with Machine Learning and Radiomics for Accurate and Rapid Early Diagnosis of COVID-19 from Chest X-ray

2020 ◽  
Author(s):  
Mahbubunnabi Tamal ◽  
Maha Alshammari ◽  
Meernah Alabdullah ◽  
Rana Hourani ◽  
Hossain Abu Alola ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Diagnosis ◽  
Cost Effective ◽  
The Other ◽  
Support Vector ◽  
Rt Pcr ◽  
Data Set ◽  
X Ray ◽  
Other Hand ◽  
Chest X Ray

ABSTRACTEarly diagnosis of COVID-19 is considered the first key action to prevent spread of the virus. Currently, reverse transcription-polymerase chain reaction (RT-PCR) is considered as a gold standard point-of-care diagnostic tool. However, several limitations of RT-PCR have been identified, e.g., low sensitivity, cost, long delay in getting results and the need of a professional technician to collect samples. On the other hand, chest X-ray (CXR) is routinely used as a cost-effective diagnostic test for diagnosis and monitoring different respiratory abnormalities and is currently being used as a discriminating tool for COVID-19. However, visual assessment of CXR is not able to distinguish COVID-19 from other lung conditions. Several machine learning algorithms have been proposed to detect COVID-19 directly from CXR images with reasonably good accuracy on a data set that was randomly split into two subsets for training and test. Since these methods require a huge number of images for training, data augmentation with geometric transformation was applied to increase the number of images. It is highly likely that the images of the same patients are present in both the training and test sets resulting in higher accuracies in detection of COVID-19. It is, therefore, vital to assess the performance of COVID-19 detection algorithm on an independent data set with different degrees of the disease before being employed for clinical settings. On the other hand, machine learning techniques that depend on handcrafted features extraction and selection approaches can be trained with smaller data set. The features can also be analyzed separately for various lung conditions. Radiomics features are such kind of handcrafted features that represent heterogeneous appearance of the lung on CXR quantitatively and can be used to distinguish COVID-19 from other lung conditions. Based on this hypothesis, a machine learning based technique is proposed here that is trained on a set of suitable radiomics features (71 features) to detect COVID-19. It is found that Support Vector Machine (SVM) and Ensemble Bagging Model Trees (EBM) trained on these 71 radiomics features can distinguish between COVID-19 and other diseases with an overall sensitivity of 99.6% and 87.8% and specificity of 85% and 97% respectively. Though the performance is comparable for both methods, EBM is more robust across severity levels. Severity, in this case, was scored between 0 to 4 by two experienced radiologists for each lung segment of each CXR image represents the degree of severity of the disease. For the case of 0 severity, sensitivity and specificity of the EBM method are 91.7% and 100% respectively indicating that there are certain radiomics pattern that are not visibly distinguishable. Since the proposed method does not require any manual intervention (e.g., sample collection etc.), it can be integrated with any standard X-ray reporting system to be used as an efficient, cost-effective and rapid early diagnosis device. It can also be deployed in places where quick results of the COVID-19 test are required, e.g., airports, seaports, hospitals, health clinics, etc.

scholarly journals Explainable Machine Learning for COVID-19 Pneumonia Classification With Texture-Based Features Extraction in Chest Radiography

2022 ◽  
Vol 3 ◽  
Author(s):  
Luís Vinícius de Moura ◽  
Christian Mattjie ◽  
Caroline Machado Dartora ◽  
Rodrigo C. Barros ◽  
Ana Maria Marques da Silva
Keyword(s):  
Machine Learning ◽  
Random Search ◽  
Texture Features ◽  
Classification Model ◽  
Recursive Feature Elimination ◽  
X Rays ◽  
X Ray ◽  
Reverse Transcription Pcr ◽  
Ensemble Machine Learning ◽  
Chest X Ray

Both reverse transcription-PCR (RT-PCR) and chest X-rays are used for the diagnosis of the coronavirus disease-2019 (COVID-19). However, COVID-19 pneumonia does not have a defined set of radiological findings. Our work aims to investigate radiomic features and classification models to differentiate chest X-ray images of COVID-19-based pneumonia and other types of lung patterns. The goal is to provide grounds for understanding the distinctive COVID-19 radiographic texture features using supervised ensemble machine learning methods based on trees through the interpretable Shapley Additive Explanations (SHAP) approach. We use 2,611 COVID-19 chest X-ray images and 2,611 non-COVID-19 chest X-rays. After segmenting the lung in three zones and laterally, a histogram normalization is applied, and radiomic features are extracted. SHAP recursive feature elimination with cross-validation is used to select features. Hyperparameter optimization of XGBoost and Random Forest ensemble tree models is applied using random search. The best classification model was XGBoost, with an accuracy of 0.82 and a sensitivity of 0.82. The explainable model showed the importance of the middle left and superior right lung zones in classifying COVID-19 pneumonia from other lung patterns.

scholarly journals COVID-19 Identification using Machine Learning Classifiers with GLCM Features of Chest X-ray Images

2021 ◽  
Vol 18 (23) ◽  
pp. 46
Author(s):  
Sudeep D. Thepade ◽  
Hrishikesh Jha
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Automated System ◽  
Learning Models ◽  
X Ray ◽  
Chest X Ray ◽  
Rapid Pace ◽  
Occurrence Matrix ◽  
Machine Learning Models

COVID-19 is an ongoing pandemic, and is also known by the name coronavirus. It was originally discovered in Wuhan, China, in December, 2019. Since then, it has been increasing rapidly worldwide. Since it has been increasing at such a rapid pace, testing equipment has limited availability. Also, this disease spreads very quickly, so it is better if it is detected earlier, in order so that it can be stopped from spreading. Therefore, the importance of early detection has increased; however, because of the shortage of testing sets, it is a necessity to develop an automated system that can detect whether the COVID-19 disease is present in a person or not as early as possible. Therefore, in this work, to extract features from X-ray images of the chest, we have made use of the Gray Level Co-occurrence Matrix (GLCM). After extracting these features for the classification of the images, we used different machine learning models, and an ensemble of machine learning models, to classify X-ray images of the chest as COVID-19, Normal, Pneumonia-bac, or Pneumonia-vir. Considering the average of performance metrics, the ensemble of Random Forest-MLP gave the best result among the variations.

scholarly journals A novel machine learning‐based analytical framework for automatic detection of COVID ‐19 using chest X‐ray images

2021 ◽  
Author(s):  
Shikhar Johri ◽  
Mehendi Goyal ◽  
Sahil Jain ◽  
Manoj Baranwal ◽  
Vinay Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Automatic Detection ◽  
Analytical Framework ◽  
X Ray ◽  
Chest X Ray

scholarly journals An Integrated Framework with Machine Learning and Radiomics for Accurate and Rapid Early Diagnosis of COVID-19 from Chest X-ray

2021 ◽  
pp. 115152
Author(s):  
Mahbubunnabi Tamal ◽  
Maha Alshammari ◽  
Meernah Alabdullah ◽  
Rana Hourani ◽  
Hossain Abu Alola ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Diagnosis ◽  
X Ray ◽  
Integrated Framework ◽  
Chest X Ray

scholarly journals Swarm Learning for decentralized and confidential clinical machine learning

Nature ◽  
2021 ◽  
Author(s):  
Stefanie Warnat-Herresthal ◽  
◽  
Hartmut Schultze ◽  
Krishnaprasad Lingadahalli Shastry ◽  
Sathyanarayanan Manamohan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Precision Medicine ◽  
Distributed Data ◽  
X Ray ◽  
Uniform Distributions ◽  
Privacy Laws ◽  
Data Owner ◽  
Machine Learning Approach ◽  
Chest X Ray ◽  
Privacy Legislation

AbstractFast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.

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