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 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 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 One-shot Cluster-Based Approach for the Detection of COVID–19 from Chest X–ray Images

2021 ◽  
Author(s):  
V. N. Manjunath Aradhya ◽  
Mufti Mahmud ◽  
D. S. Guru ◽  
Basant Agarwal ◽  
M. Shamim Kaiser
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Probabilistic Neural Network ◽  
Classification Model ◽  
Superior Performance ◽  
Generalized Regression Neural Network ◽  
X Rays ◽  
X Ray ◽  
Proposed Model ◽  
Chest X Ray

AbstractCoronavirus disease (COVID-19) has infected over more than 28.3 million people around the globe and killed 913K people worldwide as on 11 September 2020. With this pandemic, to combat the spreading of COVID-19, effective testing methodologies and immediate medical treatments are much required. Chest X-rays are the widely available modalities for immediate diagnosis of COVID-19. Hence, automation of detection of COVID-19 from chest X-ray images using machine learning approaches is of greater demand. A model for detecting COVID-19 from chest X-ray images is proposed in this paper. A novel concept of cluster-based one-shot learning is introduced in this work. The introduced concept has an advantage of learning from a few samples against learning from many samples in case of deep leaning architectures. The proposed model is a multi-class classification model as it classifies images of four classes, viz., pneumonia bacterial, pneumonia virus, normal, and COVID-19. The proposed model is based on ensemble of Generalized Regression Neural Network (GRNN) and Probabilistic Neural Network (PNN) classifiers at decision level. The effectiveness of the proposed model has been demonstrated through extensive experimentation on a publicly available dataset consisting of 306 images. The proposed cluster-based one-shot learning has been found to be more effective on GRNN and PNN ensembled model to distinguish COVID-19 images from that of the other three classes. It has also been experimentally observed that the model has a superior performance over contemporary deep learning architectures. The concept of one-shot cluster-based learning is being first of its kind in literature, expected to open up several new dimensions in the field of machine learning which require further researching for various applications.

scholarly journals Lung Ultrasound: new Opportunities for a Cardiologist

Kardiologiia ◽  
2020 ◽  
Vol 60 (1) ◽  
pp. 81-92
Author(s):  
A. Z. Arystan ◽  
Y. T. Khamzina ◽  
V. V. Benberin ◽  
D. V. Fettser ◽  
Y. N. Belenkov
Keyword(s):  
Lung Ultrasound ◽  
Ultrasound Examination ◽  
Cost Effective ◽  
Diagnostic Value ◽  
Ultrasound Finding ◽  
Time Saving ◽  
X Ray ◽  
Cardiac Decompensation ◽  
Chest X Ray

This review focused on ultrasound examination of lungs, a useful complement to transthoracic echocardiography (EchoCG), which is superior to chest X-ray in the diagnostic value. The lung acoustic window always remains open and allows obtaining high-quality images in most cases. For a cardiologist, the major points of the method application are determination of pleural effusion and lung congestion. This method has a number of advantages: it is time-saving; cost-effective; portable and accessible; can be used in a real-time mode; not associated with radiation; reproducible; and highly informative. The ultrasound finding of wet lungs would indicate threatening, acute cardiac decompensation long before appearance of clinical, auscultative, and radiological signs of lung congestion. Modern EchoCG should include examination of the heart and lungs as a part of a single, integrative ultrasound examination.

scholarly journals COVID Detection From Chest X-ray Using MobileNet and Residual Separable Convolution Block

2021 ◽  
Author(s):  
Jagadeesh Kakarla ◽  
Bala Venkateswarlu Isunuri
Keyword(s):  
Mobile Applications ◽  
Cost Effective ◽  
Superior Performance ◽  
Training Time ◽  
X Ray ◽  
Proposed Model ◽  
The Social ◽  
The World ◽  
Chest X Ray ◽  
Detection And Diagnosis

Abstract A newly emerged coronavirus disease affects the social and economical life of the world. This virus mainly infects the respiratory system and spreads with airborne communication. Several countries witness the serious consequences of the COVID pandemic. Early detection of COVID infection is the critical step to survive a patient from death. The chest radiography examination is the fast and cost-effective way for COVID detection. Several researchers have been motivated to automate COVID detection and diagnosis process using chest X-ray images. However, existing models employ deep networks and are suffering from high training time. This work presents transfer learning and residual separable convolution block for COVID detection. The proposed model utilizes pre-trained MobileNet for binary image classification. The proposed residual separable convolution block has improved the performance of basic MobileNet. Two publicly available datasets COVID5K, and COVIDRD have considered for the evaluation of the proposed model. Our proposed model exhibits superior performance than existing state-of-art and pre-trained models with 99% accuracy on both datasets. We have achieved similar performance on noisy datasets. Moreover, the proposed model outperforms existing pre-trained models with less training time and competitive performance than basic MobileNet. Further, our model is suitable for mobile applications as it uses fewer parameters and lesser training time.

scholarly journals CoroNet: A Deep Network Architecture for Semi-Supervised Task-Based Identification of COVID-19 from Chest X-ray Images

2020 ◽  
Author(s):  
Shahin Khobahi ◽  
Chirag Agarwal ◽  
Mojtaba Soltanalian
Keyword(s):  
Network Architecture ◽  
Virus Disease ◽  
Cost Effective ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Rt Pcr ◽  
X Ray ◽  
Chest X Ray ◽  
Pcr Test

AbstractIn late 2019, a new Coronavirus disease, referred to as Corona virus disease 2019 (COVID-19), emerged in Wuhan city, Hubei, China, and resulted in a global pandemic—claiming a large number of lives and affecting billions all around the world. The current global standard used in diagnosis of COVID-19 in suspected cases is the real-time polymerase chain reaction (RT-PCR) test. Although the RT-PCR remains the standard reference for diagnosis purposes, it is a time-consuming and expensive test, and moreover, it usually suffers from high rates of false-negatives. Several early works have reported that the sensitivity of the chest Computed Tomography (CT) and the chest X-ray imaging are noticeably greater than that of the RT-PCR test at the initial representations of the disease, making them great candidates for developing new and sophisticated methodologies for analysis and classification of COVID-19 cases. In this paper, we establish the use of a rapid, non-invasive and cost-effective X-ray-based method as a key diagnosis and screening tool for COVID-19 at early and intermediate stages of the disease. To this end, we develop a novel and sophisticated deep learning-based signal and image processing technique as well as classification methodology for analyzing X-ray images specific to COVID-19 disease. Specifically, we consider a semi-supervised learning methodology based on AutoEncoders to first extract the infected legions in chest X-ray manifestation of COVID-19 and other Pneumonia-like diseases (as well as healthy cases). Then, we utilize this highly-tailored deep architecture to extract the relevant features specific to each class (i.e., healthy, non-COVID pneumonia, and COVID-19) and train a powerful yet efficient classifier to perform the task of automatic diagnosis. Furthermore, the semi-supervised nature of the proposed framework enables us to efficiently exploit the limited available dataset on COVID-19 while exploiting the vast amount of available X-ray dataset for healthy and non-COVID classes. Moreover, such a semi-supervised approach does not require an expert-annotated lesion area for each class. Our numerical investigations demonstrate that the proposed framework outperforms the state-of-the-art methods for COVID-19 identification while employing approximately ten times fewer training parameters as compared to other existing methodologies for classification of the COVID-19 from X-ray images (facilitating efficient training in a limited data regime). We further develop explainable artificial intelligence tools that can explain the diagnosis by using attribution maps while providing an indispensable tool for the radiologist in triage state. We have made the codes of our proposed framework publicly available to the research and healthcare community1.

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
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