scholarly journals Multiple Lung Diseases Classification from Chest X- Ray Images using Deep Learning approach

2021 ◽  
Vol 10 (5) ◽  
pp. 2936-2946
Keyword(s):  
Deep Learning ◽  
Lung Diseases ◽  
Medical Center ◽  
Radiology Department ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Low Resource Settings ◽  
X Ray ◽  
Low Resource ◽  
Chest X Ray

Lung diseases are disorders in the lung that affects proper functioning of the breathing system. Chronic Obstructive Pulmonary Disease, lung Cancer, pneumonia, tuberculosis, and pneumothorax are prevalent in most developing countries. Diagnosis of lung diseases is usually performed through visual inspection of chest X- ray images, especially in low resource settings. This procedure is time consuming, tedious, and subjected to inter- and intra-observer variability leading to misdiagnosis. The purpose of this research was to develop a method for automatic classification of multiple lung disease from chest X-ray images using Xception deep learning method. The data required for training, validation and testing the system was collected from Jimma University Medical Center Radiology Department and National Institute of Health (NIH) chest X-ray dataset repository. All the images have been pre- processed prior to training. An accuracy, sensitivity, and specificity of 97.3%, 97.2%, and 99.4%, respectively have been achieved for multi-class classification. The developed system can be used as a decision support system for physicians, especially those in low resource settings where both the expertise and the means is in scarce. The system also allows capturing of images from radiographic films extending its implementation in areas where only the conventional X-ray machines are available.

scholarly journals Roflumilast succeeds where traditional therapy fails: a case report

2015 ◽  
Vol 79 (3-4) ◽  
Author(s):  
Gabriella Guarnieri
Keyword(s):  
Inhaled Corticosteroids ◽  
Chronic Obstructive ◽  
High Dose ◽  
Welding Fumes ◽  
Obstructive Pulmonary Disease ◽  
Once Daily ◽  
X Ray ◽  
Clinical Control ◽  
History Of ◽  
Chest X Ray

The case of a 72-year-old man with a long history of chronic obstructive pulmonary disease (COPD, patient D according to Guidelines GOLD 2013) in a subject professionally exposed to welding fumes is presented. Diagnosis was based on symptoms and spirometry and confirmed by chest X-ray examination. Since 1997 the patient has been under different therapies, including high-dose inhaled corticosteroids and bronchodilators, with poor clinical control and frequent exacerbations. Roflumilast 500 μg once daily was started in January 2012 and patient’s respiratory symptoms, number of exacerbations and spirometry values have gradually improved since then. Roflumilast was an effective treatment in this case of difficult to treat severe COPD.

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

2020 ◽  
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.

scholarly journals Study on prevalence of left ventricular diastolic dysfunction in chronic obstructive pulmonary disease

2019 ◽  
Vol 6 (2) ◽  
pp. 222
Author(s):  
Mohamed Rafi ◽  
Monna Mohamed Jaber
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Diastolic Dysfunction ◽  
Left Ventricular Diastolic Dysfunction ◽  
Left Ventricular ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
X Ray ◽  
Copd Patients ◽  
Ventricular Diastolic Dysfunction ◽  
Chest X Ray

Background: Left ventricular diastolic dysfunction (LVD) is very common in chronic obstructive pulmonary disease (COPD) patients. The aim of the present study was to LVD function in COPD patients using echocardiogram and to detect the presence of LVD dysfunction in all stages of COPD.Methods: This was an observational study done at Institute of Internal Medicine, Madras Medical College and Rajiv Gandhi Government General Hospital, Chennai, during the period from March 2015 to August 2015. The study included 100 patients with COPD. All patients were subjected to chest X-ray, electrocardiogram, pulmonary function test (PFT) and echocardiogram.Results: The results showed the prevalence rate of LVD dysfunction in 80% patients with COPD. There was a good significant correlation between age (p<0.001), status of smoking (p<0.05), chest X-ray findings (p<0.001) and stages of COPD (p<0.001) with incidence of LVD in study population.Conclusions: High prevalence of LVD dysfunction was noticed in patients with COPD. Hence patients with COPD should undergo routine examinations timely to prevent the incidence of associated cardiovascular complications.

scholarly journals Medical Image of the Month: Cavitating Pseudomonas aeruginosa Pneumonia

2021 ◽  
Vol 23 (5) ◽  
pp. 126-128
Author(s):  
Grigory Kildaze ◽  
Keyword(s):  
Medical Image ◽  
White Blood Cells ◽  
Chronic Obstructive ◽  
Past Medical History ◽  
C Reactive Protein ◽  
Obstructive Pulmonary Disease ◽  
X Ray ◽  
Reactive Protein ◽  
Blood Tests ◽  
Chest X Ray

No abstract available. Article truncated after 150 words. A 56-year-old woman presented with cough and shortness of breath to hospital. She had a temperature of 39.2°C and had recently completed course of steroids and antibiotics for exacerbation of chronic obstructive pulmonary disease (COPD). She was an active smoker of 15 cigarettes/day for about 40 years. No other past medical history was noted. On examination she had left-sided crepitations and oxygen saturations of 90% on room air. Chest x-ray (CXR) (Fig 1:A) showed features of background emphysema with upper lobe peripheral bullae, larger on the left. Dense left peri-hilar consolidation was also described. SARS-CoV-2 swab was negative. White blood cells (WBC) were raised at 16.9x109/L and C-reactive protein (CRP) at 331 mg/L. The rest of the blood tests were unremarkable. CURB-65 score was zero but treatment was commenced with intravenous (IV) amoxicillin & oral clarithromycin in view of level of CRP and CXR findings. On Day 4 of admission …

scholarly journals Chest X-Ray Classification of Lung Diseases Using Deep Learning

2021 ◽  
Vol 1 (1) ◽  
pp. 12-18
Author(s):  
Yew Fai Cheah
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Lung Disease ◽  
Lung Diseases ◽  
Viral Pneumonia ◽  
Normal Lung ◽  
X Ray ◽  
Chest X Ray ◽  
Online Sources

Chest X-ray images can be used to detect lung diseases such as COVID-19, viral pneumonia, and tuberculosis (TB). These diseases have similar patterns and diagnoses, making it difficult for clinicians and radiologists to differentiate between them. This paper uses convolutional neural networks (CNNs) to diagnose lung disease using chest X-ray images obtained from online sources. The classification task is separated into three and four classes, with COVID-19, normal, TB, and viral pneumonia, while the three-class problem excludes the normal lung. During testing, AlexNet and ResNet-18 gave promising results, scoring more than 95% accuracy.

Detection of peripherally inserted central catheter (PICC) in chest X-ray images: A multi-task deep learning model

2020 ◽  
Vol 197 ◽  
pp. 105674
Author(s):  
Dingding Yu ◽  
Kaijie Zhang ◽  
Lingyan Huang ◽  
Bonan Zhao ◽  
Xiaoshan Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Peripherally Inserted Central Catheter ◽  
Central Catheter ◽  
X Ray ◽  
Chest X Ray ◽  
Deep Learning Model

scholarly journals Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19

2021 ◽  
Author(s):  
Abdullahi Umar Ibrahim ◽  
Mehmet Ozsoz ◽  
Sertan Serte ◽  
Fadi Al-Turjman ◽  
Polycarp Shizawaliyi Yakoi
Keyword(s):  
Deep Learning ◽  
X Ray ◽  
Chest X Ray
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