scholarly journals One Shot Cluster Based Approach for the Detection of COVID-19 from Chest X-Ray Images

2020 ◽  
Author(s):  
V.N. Manjunath Aradhya ◽  
Mufti Mahmud ◽  
Basant Agarwal ◽  
D.S. Guru ◽  
M. Shamim Kaiser
Keyword(s):  
Virus Disease ◽  
X Rays ◽  
Probabilistic Neural Networks ◽  
X Ray ◽  
The Core ◽  
Wide Range ◽  
Test Instance ◽  
Chest X Ray ◽  
Detection And Diagnosis ◽  
The Way

Corona virus disease (COVID-19) has infected over more than 10 million people around the globe and killed at least 500K worldwide by the end of June 2020. As this disease continues to evolve and scientists and researchers around the world now trying to find out the way to combat this disease in most effective way. Chest X-rays are widely available modality for immediate care in diagnosing COVID-19. Precise detection and diagnosis of COVID-19 from these chest X-rays would be practical for the current situation. This paper proposes one shot cluster based approach for the accurate detection of COVID-19 chest x-rays. The main objective of one shot learning (OSL) is to mimic the way humans learn in order to make classification or prediction on a wide range of similar but novel problems. The core constraint of this type of task is that the algorithm should decide on the class of a test instance after seeing just one test example. For this purpose we have experimented with widely known Generalized Regression and Probabilistic Neural Networks. Experiments conducted with publicly available chest x-ray images demonstrate that the method can detect COVID-19 accurately with high precision. The obtained results have outperformed many of the convolutional neural network based existing methods proposed in the literature.

scholarly journals Chest X-ray image analysis and classification for COVID-19 pneumonia detection using Deep CNN

2020 ◽  
Author(s):  
Terry Gao ◽  
Grace Wang
Keyword(s):  
Training Dataset ◽  
X Rays ◽  
X Ray ◽  
Speed Up ◽  
Chest X Ray ◽  
Coronavirus Infection ◽  
Deep Cnn ◽  
Detection And Diagnosis ◽  
The Individual ◽  
New Diagnosis

Abstract To speed up the discovery of COVID-19 disease mechanisms, this research developed a new diagnosis platform using a deep convolutional neural network (CNN) that is able to assist radiologists with diagnosis by distinguishing COVID-19 pneumonia from non-COVID-19 pneumonia in patients at Middlemore Hospital based on chest X-ray classification and analysis. Such a tool can save time in interpreting chest X-rays and increase the accuracy and thereby enhance our medical capacity for the detection and diagnosis of COVID-19. The research idea is that a set of X-ray medical lung images (which include normal, infected by bacteria, infected by virus including COVID-19) were used to train a deep CNN that can distinguish between the noise and the useful information and then uses this training to interpret new images by recognizing patterns that indicate certain diseases such as coronavirus infection in the individual images. The supervised learning method is used as the process of learning from the training dataset and can be thought of as a doctor supervising the learning process. It becomes more accurate as the number of analyzed images grows. In this way, it imitates the training for a doctor, but the theory is that since it is capable of learning from a far larger set of images than any human, it can have the potential of being more accurate.

scholarly journals Explainable DCNN based chest X-ray image analysis and classification for COVID-19 pneumonia detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Hou ◽  
Terry Gao
Keyword(s):  
Large Scale ◽  
Rapid Screening ◽  
X Rays ◽  
X Ray ◽  
Disease Mechanisms ◽  
Average Accuracy ◽  
Speed Up ◽  
Chest X Ray ◽  
Detection And Diagnosis ◽  
New Diagnosis

AbstractTo speed up the discovery of COVID-19 disease mechanisms by X-ray images, this research developed a new diagnosis platform using a deep convolutional neural network (DCNN) that is able to assist radiologists with diagnosis by distinguishing COVID-19 pneumonia from non-COVID-19 pneumonia in patients based on chest X-ray classification and analysis. Such a tool can save time in interpreting chest X-rays and increase the accuracy and thereby enhance our medical capacity for the detection and diagnosis of COVID-19. The explainable method is also used in the DCNN to select instances of the X-ray dataset images to explain the behavior of training-learning models to achieve higher prediction accuracy. The average accuracy of our method is above 96%, which can replace manual reading and has the potential to be applied to large-scale rapid screening of COVID-9 for widely use cases.

scholarly journals Chest X-ray image analysis and classification for COVID-19 pneumonia detection using Deep CNN

2020 ◽  
Author(s):  
Terry Gao
Keyword(s):  
Training Dataset ◽  
X Rays ◽  
X Ray ◽  
Average Accuracy ◽  
Speed Up ◽  
Chest X Ray ◽  
Deep Cnn ◽  
Detection And Diagnosis ◽  
The Individual ◽  
New Diagnosis

Abstract To speed up the discovery of COVID-19 disease mechanisms by X-ray images, this research developed a new diagnosis platform using a deep convolutional neural network (CNN) that is able to assist radiologists with diagnosis by distinguishing COVID-19 pneumonia from non-COVID-19 pneumonia in patients based on chest X-ray classification and analysis. Such a tool can save time in interpreting chest X-rays and increase the accuracy and thereby enhance our medical capacity for the detection and diagnosis of COVID-19. The research idea is that a set of X-ray medical lung images (which include normal, infected by bacteria, infected by virus including COVID-19) are used to train a deep CNN that can distinguish between the noise and the useful information and then uses this training to interpret new images by recognizing patterns that indicate certain diseases such as coronavirus infection in the individual images. The supervised learning method is used as the process of learning from the training dataset and can be thought of as a doctor supervising the learning process. It becomes more accurate as the number of analyzed images grows, and the average accuracy is above 95%. In this way, it imitates the training for a doctor, but the theory is that since it is capable of learning from a far larger set of images than any human, it can have the potential of being more accurate.

scholarly journals Chest X-ray image analysis and classification for COVID-19 pneumonia detection using Deep CNN

2020 ◽  
Author(s):  
Terry Gao
Keyword(s):  
Training Dataset ◽  
X Rays ◽  
X Ray ◽  
Average Accuracy ◽  
Speed Up ◽  
Chest X Ray ◽  
Deep Cnn ◽  
Detection And Diagnosis ◽  
The Individual ◽  
New Diagnosis

AbstractTo speed up the discovery of COVID-19 disease mechanisms by X-ray images, this research developed a new diagnosis platform using a deep convolutional neural network (CNN) that is able to assist radiologists with diagnosis by distinguishing COVID-19 pneumonia from non-COVID-19 pneumonia in patients based on chest X-ray classification and analysis. Such a tool can save time in interpreting chest X-rays and increase the accuracy and thereby enhance our medical capacity for the detection and diagnosis of COVID-19. The research idea is that a set of X-ray medical lung images (which include normal, infected by bacteria, infected by virus including COVID-19) are used to train a deep CNN that can distinguish between the noise and the useful information and then uses this training to interpret new images by recognizing patterns that indicate certain diseases such as coronavirus infection in the individual images. The supervised learning method is used as the process of learning from the training dataset and can be thought of as a doctor supervising the learning process. It becomes more accurate as the number of analyzed images grows, and the average accuracy is above 95%. In this way, it imitates the training for a doctor, but the theory is that since it is capable of learning from a far larger set of images than any human, it can have the potential of being more accurate.

scholarly journals 1364. Pretreatment Chest X-ray Stability Duration and Tuberculosis Disease in San Diego County, 2012–2017

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S494-S494
Author(s):  
Casey Barber ◽  
Eyal Oren ◽  
Yi-Ning Cheng ◽  
Madeline Slater ◽  
Susannah Graves
Keyword(s):  
San Diego ◽  
San Diego County ◽  
X Rays ◽  
Appreciable Change ◽  
Pulmonary Tb ◽  
X Ray ◽  
Lung Abnormalities ◽  
Wide Range ◽  
Chest X Ray ◽  
Pulmonary Status

Abstract Background Repeated chest X-rays serve as an essential screening tool to identify and describe new or stable (i.e., unchanged) lung abnormalities suggestive of pulmonary tuberculosis (TB) disease. The time for which a patient’s chest X-ray has not demonstrated appreciable change prior to treatment, or pretreatment chest X-ray stability duration, has been considered clinically useful in distinguishing inactive from active disease at four or 6 months. This relationship, however, has not been previously quantified. Methods This study relied on retrospective medical record review to assess the relationship of documented pretreatment chest X-ray stability duration thresholds relative to four and 6 months with a future clinical or culture-confirmed (Class 3) diagnosis of pulmonary TB disease. Multivariable logistic regression quantified this association among 146 patients who were evaluated and started on treatment for pulmonary TB disease in the San Diego County tuberculosis clinic between May 2012 and March 2017. Results After adjusting for age and Class B1 TB, Pulmonary status, a CXR stability duration of 4 months or more was not significantly associated with a Class 3 pulmonary TB diagnosis (adjusted odds ratio [AOR], 0.830; 95% confidence interval [CI], 0.198–3.48). Results were similar for the 6-month cut-point after adjusting for age and Class B1 Pulmonary status (AOR, 0.970; 95% CI, 0.304–3.10). Compared with less than 4 months, CXR stability durations of four to 6 months (AOR, 0.778; 95% CI, 0.156–3.89) and greater than 6 months (AOR, 0.875; 95% CI, 0.187–4.10) were also not significantly associated with a Class 3 TB diagnosis after adjusting for covariates. Conclusion Repeated chest X-rays remain a valuable tool for clinicians identifying and describing new or unchanged lung abnormalities suggestive of pulmonary TB disease. This study found no statistically significant association between pretreatment chest X-ray stability duration and subsequent TB disease diagnosis, with a wide range of estimates compatible with the data, suggesting the stability duration cut points relative to four and 6 months may not be as informative as previously understood. Disclosures All authors: No reported disclosures.

A Novel Multicolour-thresholding Auto-detection Method to Detect the Location and Severity of Inflammation in Confirmed SARS-COV-2 Cases Using Chest X-ray Images

2021 ◽  
Vol 17 ◽  
Author(s):  
Mohammed Alqahtani ◽  
Mohamed Abbas ◽  
Ali Alqahtani ◽  
Mohammad Alshahrani ◽  
Abdulhadi Alkulib ◽  
...  
Keyword(s):  
Detection Method ◽  
Color Image ◽  
Single Layer ◽  
Image Texture ◽  
Prototype Model ◽  
X Rays ◽  
X Ray ◽  
Wide Range ◽  
Chest X Ray ◽  
Severity Of The Disease

Objectives: Since late 2019, Coronavirus Disease 2019 (COVID-19) has spread around the world. It has been determined that the disease is very contagious and can cause acute respiratory distress (ARD). Medical imaging has the potential to help identify, detect, and quantify the severity of this infection. This work seeks to develop a novel auto-detection technique for verified COVID-19 cases that can detect aberrant alterations in traditional X-ray pictures. Methods: Nineteen separate-colored layers were created from X ray scans of patients diagnosed with COVID-19. Each layer represents objects that have a similar contrast and can be represented by a single color. On a single layer, objects with similar contrasts are formed. A single color image was created by extracting all the objects from all the layers. The prototype model could recognize a wide range of abnormal changes in the image texture based on color differentiation. This was true even when the contrast values of the detected uncleared abnormalities varied a little. Results: The results indicate that the proposed novel method is 91% accurate in detecting and grading COVID-19 lung infection when compared to the opinions of three experienced radiologists evaluating chest X-ray images. Additionally, the method can be used to determine the infection site and severity of the disease by categorizing the X-rays into five severity levels. Conclusion: By comparing affected tissue to healthy tissue, the proposed COVID-19 auto-detection method can identify locations and indicate the severity of the disease, as well as predict where the disease may spread.

Spontaneous Pneumomediastinum: Uncommon Cause of Air Leak in Late Preterm Neonate

2021 ◽  
Vol 35 (2) ◽  
pp. 93-94
Author(s):  
Jyotsna Bhushan ◽  
Shagufta Iqbal ◽  
Abhishek Chopra
Keyword(s):  
Respiratory Distress ◽  
Conservative Management ◽  
Preterm Neonate ◽  
Late Preterm ◽  
Intravenous Fluids ◽  
X Rays ◽  
Blood Gas ◽  
X Ray ◽  
Spontaneous Pneumomediastinum ◽  
Chest X Ray

A clinical case report of spontaneous pneumomediastinum in a late-preterm neonate, chest x-ray showing classical “spinnaker sail sign,” which was managed conservatively and had excellent prognosis on conservative management. Respiratory distress in a preterm neonate is a common clinical finding. Common causes include respiratory distress syndrome, transient tachypnea of the newborn, pneumonia, and pneumothorax. Pneumomediastinum is not very common cause of respiratory distress and more so spontaneous pneumomediastinum. We report here a preterm neonate with spontaneous pneumomediastinum who had excellent clinical recovery with conservative management. A male baby was delivered to G3P1A1 mother at 34 + 6 weeks through caesarean section done due to abruptio placenta. Apgar scores were 8 and 9. Maternal antenatal history was uneventful and there were no risk factors for early onset sepsis. Baby had respiratory distress soon after birth with Silverman score being 2/10. Baby was started on oxygen (O2) by nasal prongs through blender 0.5 l/min, FiO2 25%, and intravenous fluids. Blood gas done was normal. Possibility of transient tachypnea of newborn or mild hyaline membrane disease was kept. Respiratory distress increased at 20 h of life (Silverman score: 5), urgent chest x-ray done revealed “spinnaker sign” suggestive of pneumomediastinum, so baby was shifted to O2 by hood with FiO2 being 70%. Blood gas repeated was normal. Baby was managed conservatively on intravenous fluids and O2 by hood. Baby was gradually weaned off from O2 over next 5 days. As respiratory distress decreased, baby was started on orogastric feed, which baby tolerated well and then was switched to oral feeds. Serial x-rays showed resolution of pneumomediastinum. Baby was discharged on day 7 of life in stable condition on breast feeds and room air.

scholarly journals Accessory pathway analysis using a multimodal deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Makoto Nishimori ◽  
Kunihiko Kiuchi ◽  
Kunihiro Nishimura ◽  
Kengo Kusano ◽  
Akihiro Yoshida ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electrophysiological Study ◽  
Accessory Pathway ◽  
Learning Model ◽  
X Rays ◽  
X Ray ◽  
Wpw Syndrome ◽  
Chest X Ray ◽  
Deep Learning Model ◽  
Ecg Data

AbstractCardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

scholarly journals 3D Imaging in Unilateral Primary Pulmonary Hypoplasia in an Adult: A Case Report

2011 ◽  
Vol 2011 ◽  
pp. 1-6
Author(s):  
Aristida Georgescu ◽  
Crinu Nuta ◽  
Simona Bondari
Keyword(s):  
3D Imaging ◽  
Pulmonary Hypoplasia ◽  
Left Lung ◽  
Left Lobe ◽  
X Rays ◽  
X Ray ◽  
Female Smoker ◽  
Multidetector Computer Tomography ◽  
Biological Tests ◽  
Chest X Ray

Unilateral primary pulmonary hypoplasia is rare in adulthood (UPHA); it is characterized by a decreased number of bronchial segmentation and decreased/absent alveolar air space. Classical chest X-ray may be confusing, and the biological tests are unspecific. We present a case of UPHA in a 60-year-old female, smoker, with 3 term normal deliveries, who presented with late recurrent pneumonias and bronchiectasis-type symptomathology, arterial hypertension, and obesity. Chest X-rays revealed opacity in the left lower pulmonary zone, an apparent hypoaerated upper left lobe and left deviation of the mediastinum. Preoperatory multidetector computer tomography (MDCT) presented a small retrocardiac left lung with 5-6 bronchial segmentation range and cystic appearance. After pneumonectomy the gross specimen showed a small lung with multiple bronchiectasis and small cysts, lined by hyperplasic epithelium, surrounded by stromal fibrosclerosis. We concluded that this UPHA occurred in the 4–7 embryonic weeks, and the 3D MDCT reconstructions offered the best noninvasive diagnosis.

CHEST X RAY SCORE AND CLINICAL SEVERITY IN COVID-19 PATIENTS

2021 ◽  
pp. 31-32
Author(s):  
Sheeba Rana ◽  
Vicky Bakshi ◽  
Yavini Rawat ◽  
Zaid Bin Afroz
Keyword(s):  
Retrospective Study ◽  
Medical Records ◽  
Scoring Systems ◽  
Clinical Severity ◽  
X Rays ◽  
Antigen Test ◽  
X Ray ◽  
Classi Cation ◽  
Chest X Ray ◽  
Male Subjects

INTRODUCTION: Various chest X-ray scoring systems have been discovered and are employed to correlate with clinical severity, outcome and progression of diseases. With, the coronavirus outbreak, few chest radiograph classication were formulated, like the BSTI classication and the Brixia chest X-ray score. Brixia CXR scoring is used for assessing the clinical severity and outcome of COVID-19. This study aims to compare the Brixia CXR score with clinical severity of COVID-19 patients. MATERIAL& METHODS:This was a retrospective study in which medical records of patients aged 18 years or above, who tested for RTPCR or st st Rapid Antigen Test (RAT) for COVID positive from 1 February 2021 to 31 July 2021 (6 months) were taken. These subjects were stratied into mild, moderate and severe patients according to the ICMR guidelines. Chest X Rays were obtained and lesions were classied according to Brixia scoring system. RESULTS: Out of these 375 patients, 123 (32.8%) were female and 252 (67.2%) were male subjects. The average brixia score was 11.12. Average Brixia CXR score for mild, moderate and severe diseased subjects were 5.23, 11.20, and 14.43 respectively. DISCUSSION:The extent of chest x-ray involvement is proportional to the clinical severity of the patient. Although, a perplexing nding was that the average Brixia score of the female subjects were slightly higher than their male counterparts in the same clinical groups. CONCLUSION: Brixia CXR score correlates well with the clinical severity of the COVID-19.

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