Detection of Pneumonia Using Chest X-Ray Images and Image Processing Algorithms - A Comparative Study

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Beena Ullala Mata B N ◽  
Rishika I. S ◽  
Nikita Jain ◽  
Kaliprasad C S ◽  
Niranjan K R
Keyword(s):  
Detection Algorithm ◽  
Contour Detection ◽  
Normal Lung ◽  
Lung Region ◽  
X Rays ◽  
X Ray ◽  
Otsu Thresholding ◽  
Chest X Ray ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Utilizing exclusively picture handling procedures, this examination proposes an original strategy for distinguishing the presence of pneumonia mists in chest X-rays (CXR). Collected the several analogue chest CXRs from patients with normal and Pneumonia-infected lungs. Indigenous algorithms have been developed for cropping and for extraction of the lung region from the images. To detect pneumonia clouds first conducted the preprocessing of the image then used the image segmentation techniques like Otsu thresholding K-means clustering and global thresholding and then contour detection algorithm was applied which helped to detect lung boundary, the area’s ratio is used to classify the normal lung from pneumonia affected lung.

scholarly journals Pneumonia Detection using Artificial Neural Networks and Transfer Learning Model

2019 ◽  
Vol 8 (2S4) ◽  
pp. 149-151
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Transfer Learning ◽  
Learning Model ◽  
High Accuracy ◽  
Lung Region ◽  
X Ray ◽  
Otsu Thresholding ◽  
Chest X Ray ◽  
Healthy Lung

Developing a system that helps in detecting pneumonia in chest x-ray images of lungs at a high accuracy. Firstly, a raw image is being preprocessed with the help of Otsu Thresholding and an equalizer. This helps in detecting pneumonia clouds and identifying the ratio of healthy lung region to the total region minimum. The above task is determined by importing the original chest x-ray images in the dataset and then calculating the ratio. The preprocessed data is then fed into Inception V3 model that accurately predicts the percentage of how much pneumonia is spread. This helps in identifying pneumonia present in that area and helps determining the prescribed drugs to help them clear off the symptoms.

scholarly journals IMAGE SEGMENTATION OF CHEST X-RAYS FOR ABNORMALITY PATTERN RECOGNATION IN LUNGS USING FUZZY C-MEANS METHOD

2018 ◽  
Vol 2 (2) ◽  
pp. 13-23
Author(s):  
Matheus Alvian Wikanargo ◽  
Angelina Pramana Thenata
Keyword(s):  
Image Segmentation ◽  
The Body ◽  
Normal Lung ◽  
X Rays ◽  
Processing Stage ◽  
X Ray ◽  
Fuzzy C Means ◽  
Segmented Image ◽  
Chest X Ray ◽  
Normal Lungs

The lungs are one of the important and vital organs in the body that function as a respiratory system process. One way to detect lung disease is to do an X-rays test. Chest X-ray is a radiographic projection to detect abnormalities in lung organ by using x-ray radiation. In the process of diagnosing, doctors see the condition of the results of Chest X-rays in the form of a thorax image (chest) to know the patient has an abnormal or normal lung. However, doctors' diagnosis of chest X-rays results-based abnormalities is likely to differ depending on the doctor's abilities and experience. This problem is expected to be solved by segmenting the lung image to help make the diagnosis appropriately. The purpose of this study is to conduct an analysis that can differentiate abnormal and normal lungs. The process of recognition of these patterns consists of the pre-processing stage of image segmentation by using morphology and then proceed to grouping by using fuzzy c-means method to express the pattern of the already segmented image. This research produces normal and abnormal lung images that can be identified with an accuracy of 80%.

scholarly journals Automatic focus algorithms for TDI X-Ray image reconstruction

2012 ◽  
Vol 10 ◽  
pp. 145-151 ◽  
Author(s):  
J. Dörr ◽  
M. Rosenbaum ◽  
W. Sauer-Greff ◽  
R. Urbansky
Keyword(s):  
Food Industry ◽  
X Rays ◽  
Inverse Filtering ◽  
X Ray ◽  
Blurred Image ◽  
Time Delay And Integration ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Different Characteristics ◽  
Correct Image

Abstract. In food industry, most products are checked by X-rays for contaminations. These X-ray machines continuously scan the product passing through. To minimize the required X-ray power, a Time, Delay and Integration (TDI) CCD-sensor is used to capture the image. While the product moves across the sensor area, the X-ray angle changes during the pass. As a countermeasure, adjusting the sensor shift speed on a single focal plane of the product can be selected. However, the changing angle result in a blurred image in dependance to the thickness of the product. This so-called ''laminographic effect'' can be compensated individually for one plane by inverse filtering. As the plane of contamination is unknown, the blurred image will be inversely filtered for different planes, but only one of these images shows the correctly focussed object. If the correct image can be found, the plane containing the contamination is identified. In this contribution we demonstrate how the correctly focussed images can be found by analyzing the images of all planes. Different characteristics for correctly and incorrectly focussed planes like sharpness, number of objects and edges are investigated by using image processing algorithms.

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.

Portable Ventilation/Perfusion Scanning is Useful for Evaluating Clinically Significant Pulmonary Embolism in the ICU Despite Abnormal Chest Radiography

2018 ◽  
Vol 35 (10) ◽  
pp. 1032-1038 ◽  
Author(s):  
Aaron S. Weinberg ◽  
William Chang ◽  
Grace Ih ◽  
Alan Waxman ◽  
Victor F. Tapson
Keyword(s):  
Pulmonary Embolism ◽  
Chest Radiograph ◽  
High Probability ◽  
Hemodynamic Instability ◽  
X Rays ◽  
X Ray ◽  
Abnormal Chest ◽  
Chest X Ray ◽  
Low Probability ◽  
Clinically Significant

Objective: Computed tomography angiography is limited in the intensive care unit (ICU) due to renal insufficiency, hemodynamic instability, and difficulty transporting unstable patients. A portable ventilation/perfusion (V/Q) scan can be used. However, it is commonly believed that an abnormal chest radiograph can result in a nondiagnostic scan. In this retrospective study, we demonstrate that portable V/Q scans can be helpful in ruling in or out clinically significant pulmonary embolism (PE) despite an abnormal chest x-ray in the ICU. Design: Two physicians conducted chart reviews and original V/Q reports. A staff radiologist, with 40 years of experience, rated chest x-ray abnormalities using predetermined criteria. Setting: The study was conducted in the ICU. Patients: The first 100 consecutive patients with suspected PE who underwent a portable V/Q scan. Interventions: Those with a portable V/Q scan. Results: A normal baseline chest radiograph was found in only 6% of patients. Fifty-three percent had moderate, 24% had severe, and 10% had very-severe radiographic abnormalities. Despite the abnormal x-rays, 88% of the V/Q scans were low probability for a PE despite an average abnormal radiograph rating of moderate. A high-probability V/Q for PE was diagnosed in 3% of the population despite chest x-ray ratings of moderate to severe. Six patients had their empiric anticoagulation discontinued after obtaining the results of the V/Q scan, and no anticoagulation was started for PE after a low-probability V/Q scan. Conclusion: Despite the large percentage of moderate-to-severe x-ray abnormalities, PE can still be diagnosed (high-probability scan) in the ICU with a portable V/Q scan. Although low-probability scans do not rule out acute PE, it appeared less likely that any patient with a low-probability V/Q scan had severe hypoxemia or hemodynamic instability due to a significant PE, which was useful to clinicians and allowed them to either stop or not start anticoagulation.

scholarly journals Objective Evaluation of Deep Uncertainty Predictions for COVID-19 Detection

2021 ◽  
Author(s):  
Hamzeh Asgharnezhad ◽  
Afshar Shamsi ◽  
Roohallah Alizadehsani ◽  
Abbas Khosravi ◽  
Saeid Nahavandi ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
Objective Evaluation ◽  
X Rays ◽  
The Novel ◽  
X Ray ◽  
Uncertainty Estimates ◽  
Chest X Ray ◽  
Novel Concept

Abstract Deep neural networks (DNNs) have been widely applied for detecting COVID-19 in medical images. Existing studies mainly apply transfer learning and other data representation strategies to generate accurate point estimates. The generalization power of these networks is always questionable due to being developed using small datasets and failing to report their predictive confidence. Quantifying uncertainties associated with DNN predictions is a prerequisite for their trusted deployment in medical settings. Here we apply and evaluate three uncertainty quantification techniques for COVID-19 detection using chest X-Ray (CXR) images. The novel concept of uncertainty confusion matrix is proposed and new performance metrics for the objective evaluation of uncertainty estimates are introduced. Through comprehensive experiments, it is shown that networks pertained on CXR images outperform networks pretrained on natural image datasets such as ImageNet. Qualitatively and quantitatively evaluations also reveal that the predictive uncertainty estimates are statistically higher for erroneous predictions than correct predictions. Accordingly, uncertainty quantification methods are capable of flagging risky predictions with high uncertainty estimates. We also observe that ensemble methods more reliably capture uncertainties during the inference. DNN-based solutions for COVID-19 detection have been mainly proposed without any principled mechanism for risk mitigation. Previous studies have mainly focused on on generating single-valued predictions using pretrained DNNs. In this paper, we comprehensively apply and comparatively evaluate three uncertainty quantification techniques for COVID-19 detection using chest X-Ray images. The novel concept of uncertainty confusion matrix is proposed and new performance metrics for the objective evaluation of uncertainty estimates are introduced for the first time. Using these new uncertainty performance metrics, we quantitatively demonstrate where and when we could trust DNN predictions for COVID-19 detection from chest X-rays. It is important to note the proposed novel uncertainty evaluation metrics are generic and could be applied for evaluation of probabilistic forecasts in all classification problems.

scholarly journals Role of Chest X-Ray in Coronavirus Disease and Correlation of Radiological Features with Clinical Outcomes in Indian Patients

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Satyanand Sathi ◽  
Richa Tiwari ◽  
Savita Verma ◽  
Anil Kumar Garg ◽  
Virendra Singh Saini ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Hospital Stay ◽  
Rank Correlation ◽  
X Rays ◽  
X Ray ◽  
Radiological Features ◽  
Spearman’S Rank Correlation ◽  
Spearman’S Rank Correlation Coefficient ◽  
Discharged Patients ◽  
Chest X Ray

Recent literature has reported that radiological features of coronavirus disease (COVID-19) patients are influenced by computed tomography. This study aimed to assess the characteristic chest X-ray features of COVID-19 and correlate them with clinical outcomes of patients. This retrospective study included 120 COVID-19 patients. Baseline chest X-rays and serial chest X-rays were reviewed. A severity index in the form of maximum radiological assessment of lung edema (RALE) score was calculated for each lung, and scores of both the lungs were summed to obtain a final score. The mean ± standard deviation (SD) and frequency (%) were determined, and an unpaired t test, Spearman’s rank correlation coefficient, and logistic regression analyses were performed for statistical analyses. Among 120 COVID-19 patients, 74 (61.67%) and 46 (38.33%) were males and females, respectively; 64 patients (53.33%) had ground-glass opacities (GGO), 55 (45.83%) had consolidation, and 38 (31.67%) had reticular-nodular opacities, with lower zone distribution (50%) and peripheral distribution (41.67%). Baseline chest X-ray showed a sensitivity of 63.3% in diagnosing typical findings of SARS-CoV-2 pneumonia. The maximum RALE score was 2.13 ± 1.9 in hospitalized patients and 0.57 ± 0.77 in discharged patients ( p value <0.0001). Spearman’s rank correlation coefficient between maximum RALE score and clinical outcome parameters was as follows: age, 0.721 ( p value <0.00001); >10 days of hospital stay, 0.5478 ( p value <0.05); ≤10 days of hospital stay, 0.5384 ( p value <0.0001); discharged patients, 0.5433 ( p value <0.0001); and death, 0.6182 ( p value = 0.0568). The logistic regression analysis revealed that maximum RALE scores (0.0932 [0.024–0.367]), (10.730 [2.727–42.206]), (1.258 [0.990–1.598]), and (0.794 [0.625–1.009]) predicted discharge, death, >10 days of hospital stay, and ≤10 days of hospital stay, respectively. The study findings suggested that the RALE score can quantify the extent of COVID-19 and can predict the prognosis of patients.

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