scholarly journals Effective Survey on Detection and Classification of COVID-19 Suspected Individual Using CT scan Images

2021 ◽  
pp. 294-299
Author(s):  
Snehal R. Sambhe ◽  
Dr. Kamlesh A. Waghmare
Keyword(s):  
Ct Scan ◽  
X Rays ◽  
Rt Pcr ◽  
X Ray ◽  
Feature Extractor ◽  
Chest X Ray ◽  
Coronavirus Infection ◽  
Aided Diagnosis ◽  
Excessive Risk

As insufficient testing kits are available, the development of new testing kits for detecting COVID remains an open vicinity of research. It’s impossible to test each and every patient suffering from coronavirus symptoms using the traditional method i.e. RT-PCR. This test requires more time to produce results and have less sensitivity. Detecting feasible coronavirus infection using chest X-Ray may also assist quarantine excessive risk sufferers while testing results are disclosed. A learning model can be built based on CT scan images or Chest X-rays of individuals with higher accuracy. This paper represents a computer-aided diagnosis of COVID 19 infection bases on a feature extractor by using CNN models.

scholarly journals COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization

2020 ◽  
Author(s):  
Tahmina Zebin ◽  
Shahadate Rezvy ◽  
Wei Pang
Keyword(s):  
Disease Progression ◽  
Detection Accuracy ◽  
X Rays ◽  
Automated Classification ◽  
X Ray ◽  
Feature Extractor ◽  
Chest X Ray ◽  
Activation Mapping ◽  
Activation Map

Abstract Chest X-rays are playing an important role in the testing and diagnosis of COVID-19 disease in the recent pandemic. However, due to the limited amount of labelled medical images, automated classification of these images for positive and negative cases remains the biggest challenge in their reliable use in diagnosis and disease progression. We applied and implemented a transfer learning pipeline for classifying COVID-19 chest X-ray images from two publicly available chest X-ray datasets {https://github.com/ieee8023/covid-chestxray-dataset},{https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia}}. The classifier effectively distinguishes inflammation in lungs due to COVID-19 and pneumonia (viral and bacterial) from the ones with no infection (normal). We have used multiple pre-trained convolutional backbones as the feature extractor and achieved an overall detection accuracy of 91.2% , 95.3%, 96.7% for the VGG16, ResNet50 and EfficientNetB0 backbones respectively. Additionally, we trained a generative adversarial framework (a cycleGAN) to generate and augment the minority COVID-19 class in our approach. For visual explanations and interpretation purposes, we visualized the regions of input that are important for predictions and a gradient class activation mapping (Grad-CAM) technique is used in the pipeline to produce a coarse localization map of the highlighted regions in the image. This activation map can be used to monitor affected lung regions during disease progression and severity stages.

COVID-19 Detection on Chest X-Ray and CT Scan Images Using Multi-image Augmented Deep Learning Model

2020 ◽  
Author(s):  
Kiran Purohit ◽  
Abhishek Kesarwani ◽  
Dakshina Ranjan Kisku ◽  
Mamata Dalui
Keyword(s):  
Ct Scan ◽  
False Negative ◽  
Radical Change ◽  
Testing Time ◽  
Rt Pcr ◽  
X Ray ◽  
Negative Results ◽  
False Negative Results ◽  
Chest X Ray ◽  
Coronavirus Infection

AbstractCOVID-19 is posed as very infectious and deadly pneumonia type disease until recent time. Despite having lengthy testing time, RT-PCR is a proven testing methodology to detect coronavirus infection. Sometimes, it might give more false positive and false negative results than the desired rates. Therefore, to assist the traditional RT-PCR methodology for accurate clinical diagnosis, COVID-19 screening can be adopted with X-Ray and CT scan images of lung of an individual. This image based diagnosis will bring radical change in detecting coronavirus infection in human body with ease and having zero or near to zero false positives and false negatives rates. This paper reports a convolutional neural network (CNN) based multi-image augmentation technique for detecting COVID-19 in chest X-Ray and chest CT scan images of coronavirus suspected individuals. Multi-image augmentation makes use of discontinuity information obtained in the filtered images for increasing the number of effective examples for training the CNN model. With this approach, the proposed model exhibits higher classification accuracy around 95.38% and 98.97% for CT scan and X-Ray images respectively. CT scan images with multi-image augmentation achieves sensitivity of 94.78% and specificity of 95.98%, whereas X-Ray images with multi-image augmentation achieves sensitivity of 99.07% and specificity of 98.88%. Evaluation has been done on publicly available databases containing both chest X-Ray and CT scan images and the experimental results are also compared with ResNet-50 and VGG-16 models.

scholarly journals Radiological profile of patients undergoing Chest X-ray and computed tomography scans during COVID-19 outbreak

2021 ◽  
Vol 37 (5) ◽  
Author(s):  
Sohail Ahmed Khan ◽  
Murli Manohar ◽  
Maria Khan ◽  
Samita Asad ◽  
Syed Omair Adil
Keyword(s):  
Computed Tomography ◽  
Ct Scan ◽  
P Value ◽  
X Rays ◽  
Lung Involvement ◽  
Rt Pcr ◽  
X Ray ◽  
Computed Tomography Scans ◽  
Chest X Ray ◽  
Bilateral Lung

Background & Objective: Radiology has played a significant role in the diagnosis and quantifying the severity of COVID 19 pulmonary disease. This study was conducted to assess patterns and severity of COVID-19 pulmonary disease based on radiological imaging. Methods: A prospective observational study was conducted in a large tertiary care public sector teaching hospital of Karachi, Pakistan from June 2020 till August 2020. All confirmed and suspected COVID-19 patients referred for chest X-rays and computed tomography (CT) scans were evaluated along with RT-PCR results. Suspected patients were followed for RT-PCR. Radiological features and severity of imaging studies were determined. Results: Of 533 patients in whom X-rays were performed, majority had severe/critical findings, i.e., 304 (57.03%). Of 97 patients in whom CT scan was performed, mild/moderate findings were observed in 63 (64.94%) patients. Of 472 patients with abnormal X-rays, majority presented with alveolar pattern 459 (97.2%), bilateral lung involvement 453 (89.6%), and consolidation 356 (75.4%). Moreover, lobar predominance showed lower zone preponderance in 446 (94.5%) patients. Of 88 patients with abnormal CT findings, ground-glass opacity (GGO) 87 (98.9%) and crazy paving 69 (78.4%) were the most common findings. An insignificantly higher association of PCR positive cases was observed with severe/critical X-rays (p-value 0.076) and CT scan findings (p-value 0.431). Conclusion: Most common patterns on CT scans were GGO and crazy paving. While on chest radiographs, bilateral lung involvement with alveolar pattern and consolidation were most common findings. On X-rays, majority had severe/critical whereas CT scan had mild/moderate findings. doi: https://doi.org/10.12669/pjms.37.5.4290 How to cite this:Khan SA, Manohar M, Khan M, Asad S, Adil SO. Radiological profile of patients undergoing Chest X-ray and computed tomography scans during COVID-19 outbreak. Pak J Med Sci. 2021;37(5):---------. doi: https://doi.org/10.12669/pjms.37.5.4290 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Ultrasound Detection of Pneumothorax Compared with Chest X-Ray and Computed Tomography Scan

2011 ◽  
Vol 77 (4) ◽  
pp. 480-483 ◽  
Author(s):  
Khanjan Nagarsheth ◽  
Stanley Kurek
Keyword(s):  
Ct Scan ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Chest Ct ◽  
Trauma Patients ◽  
X Rays ◽  
Thoracic Ultrasound ◽  
X Ray ◽  
Chest X Ray ◽  
Chest Ct Scan

Pneumothorax after trauma can be a life threatening injury and its care requires expeditious and accurate diagnosis and possible intervention. We performed a prospective, single blinded study with convenience sampling at a Level I trauma center comparing thoracic ultrasound with chest X-ray and CT scan in the detection of traumatic pneumothorax. Trauma patients that received a thoracic ultrasound, chest X-ray, and chest CT scan were included in the study. The chest X-rays were read by a radiologist who was blinded to the thoracic ultrasound results. Then both were compared with CT scan results. One hundred and twenty-five patients had a thoracic ultrasound performed in the 24-month period. Forty-six patients were excluded from the study due to lack of either a chest X-ray or chest CT scan. Of the remaining 79 patients there were 22 positive pneumothorax found by CT and of those 18 (82%) were found on ultrasound and 7 (32%) were found on chest X-ray. The sensitivity of thoracic ultrasound was found to be 81.8 per cent and the specificity was found to be 100 per cent. The sensitivity of chest X-ray was found to be 31.8 per cent and again the specificity was found to be 100 per cent. The negative predictive value of thoracic ultrasound for pneumothorax was 0.934 and the negative predictive value for chest X-ray for pneumothorax was found to be 0.792. We advocate the use of chest ultrasound for detection of pneumothorax in trauma patients.

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 Unilateral Right Exophthalmia Revealing Systemic Sarcoidosis: A Case Report and a Review of the Literature

2019 ◽  
Author(s):  
Sophia Bania
Keyword(s):  
Ct Scan ◽  
Exceptional Case ◽  
Mediastinal Lymphadenopathy ◽  
Case Presentation ◽  
X Ray ◽  
Caseous Necrosis ◽  
History Of ◽  
Chest X Ray ◽  
Systemic Sarcoidosis

Background: Sarcoidosis is only revealed in 3% of the cases among Caucasians by ophthalmic damage and, when it does, it presupposes that the visceral impairment has remained silent so far. In this article, the exceptional case of a patient with systemic sarcoidosis revealed by unilateral exophthalmia is reported. Case presentation: The patient is a female with no history of substantial pathology. She had a unilateral right exophthalmia and ptosis evolving over 3 years. A dyspnea and dry cough were also reported with a duration of 1 year. The chest X-ray and CT scan revealed bilateral hilar opacities and mediastinal lymphadenopathy that lead to the suspicion of sarcoidosis. The cerebro-orbital CT scan led to the classification of the patient’s exophthalmia as Grade I and eliminated the possibility of other aetiologies. The mediastinoscopy indicated a granulomatous adenitis with no caseous necrosis, which allowed the diagnosis of a mediastinopulmonary sarcoidosis. Discussion and conclusion: The diagnostic approach to exophthalmia should involve a systematic search for sarcoidosis, although this aetiology remains exceptional.

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 Chest X-Ray findings and visual quantitative assessment of COVID-19 pneumonia

2020 ◽  
Author(s):  
Michaela Cellina ◽  
Marcello Orsi ◽  
Marta Panzeri ◽  
Giulia van der Byl ◽  
Giancarlo Oliva
Keyword(s):  
Severity Score ◽  
Quantitative Assessment ◽  
X Rays ◽  
Imaging Findings ◽  
Rt Pcr ◽  
Disease Evolution ◽  
X Ray ◽  
Radiological Score ◽  
Chest X Ray

Abstract AimTo assess the most common chest X-Ray findings and distribution in patients with confirmed diagnosis of COVID-19; to verify the repeatability of a radiological severity score, based on visual quantitative assessment; to assess the evolution of chest X-Ray findings at follow-up; to evaluate chest X-Ray sensitivity.MethodsWe analysed chest X-Rays at baseline of 110 consecutive COVID-19 patients (79 males, 31 females; mean age: 64±16 years) with RT-PCR confirmation, who presented to our ED.Two radiologists evaluated the imaging findings and distribution.A severity score, based on the extension of lung abnormalities, was assigned by two other radiologists, independently, to the baseline and follow-up X-Rays, executed in 77/110 cases; interobserver agreement was calculated. Chest X-Ray sensitivity was assessed, with RT-PCR as gold standard.ResultsInterobserver agreement was excellent for baseline and follow-up X-Rays (Cohen's K=0.989, p<0.001, Cohen's K=0.985, p<0.001, respectively). The mean score at baseline was 2.87±1.7 for readers 1 and 2. We observed radiological worsening in 52/77 (67%) patients, with significantly higher scores at follow-up (mean score: 4.27±2.15 for reader 1 and 4.28±2.14 for reader 2, respectively); p<0.001.Ground glass opacities were the most common findings (97/110, 88%). Abnormalities showed bilateral involvement in 67/110 (61%), with prevalent peripheral distribution (48/110, 43.5%).The X-Ray sensitivity for the detection of COVID-19 infection was 91%.ConclusionChest X-Ray highlighted imaging findings in line with those previously reported for chest CT. The use of a radiological score can result in clearer communication with Clinicians and a more precise assessment of disease evolution.

scholarly journals A Transfer Learning Method for Pneumonia Classification and Visualization

2020 ◽  
Vol 10 (8) ◽  
pp. 2908 ◽  
Author(s):  
Juan Luján-García ◽  
Cornelio Yáñez-Márquez ◽  
Yenny Villuendas-Rey ◽  
Oscar Camacho-Nieto
Keyword(s):  
Transfer Learning ◽  
Causes Of Death ◽  
State Of The Art ◽  
Machine Learning Algorithms ◽  
Learning Method ◽  
X Ray ◽  
Chest X Ray ◽  
Aided Diagnosis ◽  
Positive Results

Pneumonia is an infectious disease that affects the lungs and is one of the principal causes of death in children under five years old. The Chest X-ray images technique is one of the most used for diagnosing pneumonia. Several Machine Learning algorithms have been successfully used in order to provide computer-aided diagnosis by automatic classification of medical images. For its remarkable results, the Convolutional Neural Networks (models based on Deep Learning) that are widely used in Computer Vision tasks, such as classification of injuries and brain abnormalities, among others, stand out. In this paper, we present a transfer learning method that automatically classifies between 3883 chest X-ray images characterized as depicting pneumonia and 1349 labeled as normal. The proposed method uses the Xception Network pre-trained weights on ImageNet as an initialization. Our model is competitive with respect to state-of-the-art proposals. To make comparisons with other models, we have used four well-known performance measures, obtaining the following results: precision (0.84), recall (0.99), F1-score (0.91) and area under the ROC curve (0.97). These positive results allow us to consider our proposal as an alternative that can be useful in countries with a lack of equipment and specialized radiologists.

scholarly journals COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization

2020 ◽  
Author(s):  
Tahmina Zebin ◽  
Shahadate Rezvy
Keyword(s):  
Disease Progression ◽  
Input Image ◽  
Mapping Technique ◽  
Detection Accuracy ◽  
X Rays ◽  
Automated Classification ◽  
X Ray ◽  
Chest X Ray ◽  
Activation Mapping

Abstract Chest X-rays are playing an important role in the testing and diagnosis of COVID-19 disease in the recent pandemic. However, due to the limited amount of labelled medical images, automated classification of these images for positive and negative cases remains the biggest challenge in their reliable use in diagnosis and disease progression. We implemented a transfer learning pipeline for classifying COVID-19 chest X-ray images from two publicly available chest X-ray datasets1,2. The classifier effectively distinguishes inflammation in lungs due to COVID-19 and Pneumonia from the ones with no infection (normal). We have used multiple pre-trained convolutional backbones as the feature extractor and achieved an overall detection accuracy of 90%, 94.3%, and 96.8% for the VGG16, ResNet50, and EfficientNetB0 backbones respectively. Additionally, we trained a generative adversarial framework (a CycleGAN) to generate and augment the minority COVID-19 class in our approach. For visual explanations and interpretation purposes, we implemented a gradient class activation mapping technique to highlight the regions of the input image that are important for predictions. Additionally, these visualizations can be used to monitor the affected lung regions during disease progression and severity stages.

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