Chest X-Ray Image to Classify Lung diseases in Different Resolution Size using DenseNet-121 Architectures

Chest X-Ray Refresher

The Brigham Intensive Review of Internal Medicine ◽

10.1093/med/9780199358274.003.0037 ◽

2014 ◽

pp. 379-389

Author(s):

Christopher H. Fanta

Keyword(s):

Lung Diseases ◽

Interstitial Lung Diseases ◽

X Rays ◽

X Ray ◽

Obstructive Lung Diseases ◽

Chest X Ray

This Chest X-Ray Refresher is organized as a game. For each of the three topics to be discussed, we offer four chest x-rays and four clinical histories. The order of each set is random. The exercise asks that you consider the clues in the history and the findings on chest x-ray to match the history with the x-ray. In many instances, the combination will suggest a diagnosis or a limited differential of diagnostic possibilities. The three topics to be discussed are hemoptysis, chronic interstitial lung diseases, and obstructive lung diseases.

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A Study on Co-occurrence of various Lung Diseases and COVID-19 by observing Chest X-Ray Similarity using Deep Convolutional Neural Networks

2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS) ◽

10.1109/icds50568.2020.9268711 ◽

2020 ◽

Author(s):

Sashank Sridhar ◽

Siddartha Mootha ◽

Rahul Seetharaman ◽

Arockia Xavier Annie Rayan

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Lung Diseases ◽

Deep Convolutional Neural Networks ◽

X Ray ◽

Chest X Ray

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Clinical features of endobronchial tuberculosis

Vojnosanitetski pregled ◽

10.2298/vsp1402156s ◽

2014 ◽

Vol 71 (2) ◽

pp. 156-160 ◽

Cited By ~ 3

Author(s):

Natalija Samardzic ◽

Dragana Jovanovic ◽

Ljiljana Markovic-Denic ◽

Marina Roksandic-Milenkovic ◽

Spasoje Popevic ◽

...

Keyword(s):

Clinical Features ◽

Lung Diseases ◽

Tracheobronchial Tree ◽

Laboratory Findings ◽

X Ray ◽

Diagnostic Aspects ◽

Clinical Center ◽

Chest X Ray ◽

Endobronchial Tuberculosis ◽

Lung Lobes

Background/Aim. Endobronchial tuberculosis (EBTB) is a specific type of pulmonary tuberculosis which often affect the tracheobronchial tree, and can be microbiologically and/or pathohistologically confirmed. The aim of the study was to determine the clinical features and diagnostic aspects of EBTB. Methods. This retrospective study was conducted at the Clinic for Lung Diseases, Clinical Center of Serbia, Belgrade, from January 1997 to December 2007. All patients with EBTB confirmed by bronchoscopy with biopsy during a study period were analysed. Data included the patient?s medical history, a physical exam, chest X-ray, mycobacterial analysis of sputum samples, endoscopic types and patohistological confirmation. Results. In the study, 57.6% of the patients were males. The most frequent symptoms were cough (71.2%), malaise (54.2%), fever (49.2%), weight loss (40.7%), and hemoptysis (13.6%). Most of the patients were diagnosed within 30 days of symptoms onset. Sputum examination showed acid-fast bacilli in 31.4% of the patients, while sputum culture for tuberculosis bacilli were positive in 55.9% of the patients. The most common radiographic localization was in the upper lung lobes (63.5%). Cavities were present in 60.4% of the patients. The most common endoscopic subtype determined by bronchoscopy were nonspecific bronchitis (39.9%) and edematous-hyperemic subtype (36.4%). Conclusion. EBTB was more frequent among men, and among people in their fifties in our country. Detailed bronchoscopic examination, correlated with clinical and laboratory findings, will improve diagnostic rate and provide timely therapy.

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Spontaneous Pneumomediastinum – a Rare Asthma Complication

Folia Medica ◽

10.3897/folmed.61.e39419 ◽

2019 ◽

Vol 61 (3) ◽

pp. 472-477

Author(s):

Serghei Covantev ◽

Natalia Mazuruc ◽

Rasul Uzdenov ◽

Alexandru Corlateanu

Keyword(s):

Lung Diseases ◽

Acute Asthma ◽

Short Review ◽

Chronic Respiratory Disease ◽

Tracheobronchial Tree ◽

X Ray ◽

Coronary Syndrome ◽

Spontaneous Pneumomediastinum ◽

Chest X Ray ◽

Reason For Admission

Asthma is the most common chronic respiratory disease worldwide and its prevalence is increasing. Acute asthma complications are often the reason for admission to emergency healthcare service. In our article we present a case of a rare asthma complication – spontaneous pneumomediastinum with a short review of its incidence, etiology, diagnosis and management. Spontaneus pneumothorax is important to differentiate with secondary pneumomediastinum as well as other conditions as cardiac diseases (acute coronary syndrome, pericarditis, cardiac tamponade, pneumopericardium), lung diseases (pneumothorax, pulmonary embolism, tracheobronchial tree rupture), musculoskeletal disorders, and diseases of the esophagus (rupture and perforation o the esophagus). A chest X-ray is often reliable for diagnosis of spontaneous pneumomediastinum and when inconclusive, can be followed by CT. The management is usually conservative with oxygen and analgesia. Surgery is required only in cases of tracheobronchial compression. 

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Fast Deep Learning Computer-Aided Diagnosis against the Novel COVID-19 pandemic from Digital Chest X-ray Images

10.21203/rs.3.rs-36353/v1 ◽

2020 ◽

Cited By ~ 3

Author(s):

Mugahed A. Al-antari ◽

Cam-Hao Hua ◽

Sungyoung Lee

Keyword(s):

Deep Learning ◽

Lung Disease ◽

Lung Diseases ◽

Diagnostic Performance ◽

The Novel ◽

X Ray ◽

Cad System ◽

Computer Aided ◽

Chest X Ray ◽

Aided Diagnosis

Abstract Background and Objective: The novel coronavirus 2019 (COVID-19) is a harmful lung disease that rapidly attacks people worldwide. At the end of 2019, COVID-19 was discovered as mysterious lung disease in Wuhan, Hubei province of China. World health organization (WHO) declared the coronavirus outbreak a pandemic in the second week of March 2020. Simultaneous deep learning detection and classification of COVID-19 from the entire digital X-ray images is the key to efficiently assist patients and physicians for a fast and accurate diagnosis.Methods: In this paper, a deep learning computer-aided diagnosis (CAD) based on the YOLO predictor is proposed to simultaneously detect and diagnose COVID-19 among the other eight lung diseases: Atelectasis, Infiltration, Pneumothorax, Mass, Effusion, Pneumonia, Cardiomegaly, and Nodule. The proposed CAD system is assessed via five-fold tests for multi-class prediction problem using two different databases of chest X-ray images: COVID-19 and ChestX-ray8. The proposed CAD system is trained using an annotated training set of 50,490 chest X-ray images.Results: The suspicious regions of COVID-19 from the entire X-ray images are simultaneously detected and classified end-to-end via the proposed CAD predictor achieving overall detection and classification accuracies of 96.31% and 97.40%, respectively. The most testing images of COVID-19 and other lunge diseases are correctly predicted achieving intersection over union (IoU) with their GTs greater than 90%. Applying deep learning regularizers of data balancing and augmentation improve the diagnostic performance by 6.64% and 12.17% in terms of overall accuracy and F1-score, respectively. Meanwhile, the proposed CAD system presents its feasibility to diagnose the individual chest X-ray image within 0.009 second. Thus, the presented CAD system could predict 108 frames/second (FPS) at the real-time of prediction.Conclusion: The proposed deep learning CAD system shows its capability and reliability to achieve promising COVID-19 diagnostic performance among all other lung diseases. The proposed deep learning model seems reliable to assist health care systems, patients, and physicians in their practical validations.

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Evaluation of diffuse lung diseases by high resolution computed tomography of chest

International Journal of Research in Medical Sciences ◽

10.18203/2320-6012.ijrms20171282 ◽

2017 ◽

Vol 5 (4) ◽

pp. 1655

Author(s):

Poonam Vohra ◽

Harsumeet S. Sidhu

Keyword(s):

Computed Tomography ◽

High Resolution ◽

Interstitial Pneumonia ◽

Lung Diseases ◽

Common Finding ◽

High Resolution Computed Tomography ◽

X Ray ◽

Chest X Ray ◽

Diffuse Lung Diseases ◽

Diffuse Lung

Background: Diffuse lung diseases describe a heterogeneous group of disorders of the lower respiratory tract characterized by inflammation and derangement of the interstitium and loss of functional alveolar units. The disease is not restricted to the interstitium only, as it involves epithelial, endothelial and mesenchymal cells with the disease process extending into the alveoli, acini and bronchioles. Thus, the entire pulmonary parenchyma is involved. The objective of the study was to evaluate diffuse lung diseases by high resolution computed tomography of chest.Methods: A cross-sectional observational study was done in 30 patients. Adult patients of either sex of age group 18 and above showing reticular opacities on chest X-ray and those patients who were incidentally diagnosed as cases of diffuse lung diseases on HRCT chest were included in present study.Results: Reticular opacities were the most common roentgenographic finding followed by reticulonodular opacities. On HRCT, intra and interlobular septal thickening was the most common finding in Idiopathic interstitial pneumonia (usual interstitial pneumonia).Conclusions: High resolution computed tomography (HRCT) is superior to the plain chest X-ray for early detection and confirmation of suspected diffuse lung diseases. In addition, HRCT allows better assessment of the extent and distribution of disease, and it is especially useful in the investigation of patients with a normal chest radiograph. Coexisting disease is often best recognized on HRCT scanning.

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Validation of the artificial intelligence software to detect chronic fibrosing interstitial lung diseases in chest X-ray

10.1183/13993003.congress-2021.oa1211 ◽

2021 ◽

Author(s):

Hirotaka Nishikiori ◽

Kenichi Hirota ◽

Tomohiro Suzuki ◽

Yuso Takagi ◽

Seiwa Honda ◽

...

Keyword(s):

Artificial Intelligence ◽

Lung Diseases ◽

Interstitial Lung Diseases ◽

X Ray ◽

Chest X Ray

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Multilayer Fractional-Order Machine Vision Classifier for Rapid Typical Lung Diseases Screening on Digital Chest X-Ray Images

IEEE Access ◽

10.1109/access.2020.3000186 ◽

2020 ◽

Vol 8 ◽

pp. 105886-105902 ◽

Cited By ~ 1

Author(s):

Jian-Xing Wu ◽

Pi-Yun Chen ◽

Chien-Ming Li ◽

Ying-Che Kuo ◽

Neng-Sheng Pai ◽

...

Keyword(s):

Machine Vision ◽

Fractional Order ◽

Lung Diseases ◽

X Ray ◽

Digital Chest ◽

Chest X Ray

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Post COVID-19 Organizing Pneumonia: The Right Time to Interfere

Medicina ◽

10.3390/medicina57030283 ◽

2021 ◽

Vol 57 (3) ◽

pp. 283

Author(s):

Kristina Bieksiene ◽

Jurgita Zaveckiene ◽

Kestutis Malakauskas ◽

Neringa Vaguliene ◽

Marius Zemaitis ◽

...

Keyword(s):

Respiratory Symptoms ◽

Lung Diseases ◽

Healthcare Professionals ◽

Lung Damage ◽

Organizing Pneumonia ◽

X Ray ◽

Management Algorithm ◽

Systemic Glucocorticoids ◽

Chest X Ray ◽

The Right

The COVID-19 pandemic dramatically changed medical care. Healthcare professionals are faced with new issues. Patients who survived COVID-19 have plenty of different continuing symptoms, of which the most common are fatigue and breathlessness. It is not well known how to care for patients with persistent or worsening respiratory symptoms and changes on chest X-ray following COVID-19 pneumonia. In this article, we talk about a subgroup of patients with organizing pneumonia following COVID-19 pneumonia that could be effectively treated with systemic glucocorticoids. It is important that patients with COVID-19 pneumonia be followed-up at least three weeks after diagnosis, in order to recognize early lung damage. We are providing a management algorithm for early diagnosis of lung diseases after COVID-19 pneumonia.

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