scholarly journals A Combined Image Segmentation and Classification Approach for COVID-19 Infected Lungs

2021 ◽  
Vol 8 (3) ◽  
pp. 71-76
Author(s):  
S.K.B. Sangeetha ◽  
Neda Afreen ◽  
Gufran Ahmad
Keyword(s):  
Image Segmentation ◽  
Learning Strategy ◽  
Medical Image Segmentation ◽  
Hybrid Swarm ◽  
X Ray ◽  
Clinical Level ◽  
Rapid Spread ◽  
Chest X Ray ◽  
Lung Infections ◽  
Tomography Scan

Lung infection or sickness is one of the most common acute ailments in humans. Pneumonia is one of the most common lung infections, and the annual global mortality rate from untreated pneumonia is increasing. Because of its rapid spread, pneumonia caused by the Coronavirus Disease (COVID-19) has emerged as a global danger as of December 2019. At the clinical level, the COVID-19 is frequently measured using a Computed Tomography Scan Slice (CTS) or a Chest X-ray. The goal of this study is to develop an image processing method for analysing COVID-19 infection in CT Scan patients. The images in this study were preprocessed using the Hybrid Swarm Intelligence and Fuzzy DPSO algorithms. According to extensive computer simulations, the persistent learning strategy for CT image segmentation using image enhancement is more efficient and adaptive than the Medical Image Segmentation (MIS) method. The findings suggest that the proposed method is more dependable, accurate, and simple than existing methods.

scholarly journals Effective Pneumothorax Detection for Chest X-Ray Images Using Local Binary Pattern and Support Vector Machine

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Yuan-Hao Chan ◽  
Yong-Zhi Zeng ◽  
Hsien-Chu Wu ◽  
Ming-Chi Wu ◽  
Hung-Min Sun
Keyword(s):  
Image Segmentation ◽  
Local Binary Pattern ◽  
Medical Image Segmentation ◽  
Support Vector ◽  
X Ray ◽  
Chest X Ray ◽  
Sobel Edge Detection ◽  
Disease Region ◽  
Treatment And Diagnosis

Automatic image segmentation and feature analysis can assist doctors in the treatment and diagnosis of diseases more accurately. Automatic medical image segmentation is difficult due to the varying image quality among equipment. In this paper, the automatic method employed image multiscale intensity texture analysis and segmentation to solve this problem. In this paper, firstly, SVM is applied to identify common pneumothorax. Features are extracted from lung images with the LBP (local binary pattern). Then, classification of pneumothorax is determined by SVM. Secondly, the proposed automatic pneumothorax detection method is based on multiscale intensity texture segmentation by removing the background and noises in chest images for segmenting abnormal lung regions. The segmentation of abnormal regions is used for texture transformed from computing multiple overlapping blocks. The rib boundaries are identified with Sobel edge detection. Finally, in obtaining a complete disease region, the rib boundary is filled up and located between the abnormal regions.

scholarly journals XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks

2021 ◽  
Author(s):  
Vishu Madaan ◽  
Aditya Roy ◽  
Charu Gupta ◽  
Prateek Agrawal ◽  
Anand Sharma ◽  
...  
Keyword(s):  
Image Classification ◽  
Second Phase ◽  
Two Phase ◽  
X Ray ◽  
The Neural Network ◽  
Rapid Spread ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Two Phases ◽  
Active Patients

AbstractCOVID-19 (also known as SARS-COV-2) pandemic has spread in the entire world. It is a contagious disease that easily spreads from one person in direct contact to another, classified by experts in five categories: asymptomatic, mild, moderate, severe, and critical. Already more than 66 million people got infected worldwide with more than 22 million active patients as of 5 December 2020 and the rate is accelerating. More than 1.5 million patients (approximately 2.5% of total reported cases) across the world lost their life. In many places, the COVID-19 detection takes place through reverse transcription polymerase chain reaction (RT-PCR) tests which may take longer than 48 h. This is one major reason of its severity and rapid spread. We propose in this paper a two-phase X-ray image classification called XCOVNet for early COVID-19 detection using convolutional neural Networks model. XCOVNet detects COVID-19 infections in chest X-ray patient images in two phases. The first phase pre-processes a dataset of 392 chest X-ray images of which half are COVID-19 positive and half are negative. The second phase trains and tunes the neural network model to achieve a 98.44% accuracy in patient classification.

scholarly journals An Adaptive Generative Adversarial Network for Cardiac Segmentation from X-ray Chest Radiographs

2020 ◽  
Vol 10 (15) ◽  
pp. 5032
Author(s):  
Xiaochang Wu ◽  
Xiaolin Tian
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Chest Radiographs ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
X Ray ◽  
Adversarial Network ◽  
Cardiac Segmentation ◽  
Adversarial Networks ◽  
Feature Extractor

Medical image segmentation is a classic challenging problem. The segmentation of parts of interest in cardiac medical images is a basic task for cardiac image diagnosis and guided surgery. The effectiveness of cardiac segmentation directly affects subsequent medical applications. Generative adversarial networks have achieved outstanding success in image segmentation compared with classic neural networks by solving the oversegmentation problem. Cardiac X-ray images are prone to weak edges, artifacts, etc. This paper proposes an adaptive generative adversarial network for cardiac segmentation to improve the segmentation rate of X-ray images by generative adversarial networks. The adaptive generative adversarial network consists of three parts: a feature extractor, a discriminator and a selector. In this method, multiple generators are trained in the feature extractor. The discriminator scores the features of different dimensions. The selector selects the appropriate features and adjusts the network for the next iteration. With the help of the discriminator, this method uses multinetwork joint feature extraction to achieve network adaptivity. This method allows features of multiple dimensions to be combined to perform joint training of the network to enhance its generalization ability. The results of cardiac segmentation experiments on X-ray chest radiographs show that this method has higher segmentation accuracy and less overfitting than other methods. In addition, the proposed network is more stable.

scholarly journals Recurrent Empyema Thoracic Secondary to Pulmonary Nocardiosis in Immunocompetent Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-5
Author(s):  
Samshol Sukahri ◽  
Lily Diana Zainudin ◽  
Mohd Firdaus Hadi ◽  
Mohd Al-Baqlish Mohd Firdaus ◽  
Muhammad Imran Abdul Hafidz
Keyword(s):  
Vital Signs ◽  
C Reactive Protein ◽  
Nocardia Species ◽  
X Ray ◽  
Reactive Protein ◽  
Pulmonary Nocardiosis ◽  
Erythrocyte Sedimentation ◽  
Chest X Ray ◽  
The Right ◽  
Tomography Scan

Pulmonary nocardiosis is a rare disorder that mainly affects immune-compromised patients. We report a 37-year-old male who presented with persistent fever associated with productive cough. During this course of therapy, he had recurrent admissions for empyema thoracic. Clinically, his vital signs were normal. Blood investigations show leukocytosis with a significantly raised erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP). Sputum acid-fast bacilli (AFB) was scanty 1+ and sputum mycobacterium culture was negative. Chest X-ray (CXR) showed consolidative changes with mild to moderate pleural effusion on the right side. Skin biopsy was taken and showed Paecilomyces species. A computed tomography scan (CT thorax) was performed and revealed a multiloculated collection within the right hemithorax with a split pleura sign. Decortications were performed and tissue culture and sensitivity (C+S) growth of Nocardia species. And it is sensitive to sulfamethoxazole-trimethoprim and completed treatment for 4 months. This case highlights that pulmonary nocardiosis should be kept in mind in also immune-competent patients, especially in suspected cases of tuberculosis not responding to antitubercular therapy.

scholarly journals COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images

2020 ◽  
Vol 10 (16) ◽  
pp. 5683 ◽  
Author(s):  
Lourdes Duran-Lopez ◽  
Juan Pedro Dominguez-Morales ◽  
Jesús Corral-Jaime ◽  
Saturnino Vicente-Diaz ◽  
Alejandro Linares-Barranco
Keyword(s):  
Deep Learning ◽  
Imaging Techniques ◽  
Learning System ◽  
Screening Process ◽  
X Ray ◽  
Average Accuracy ◽  
Rapid Spread ◽  
Chest X Ray ◽  
And Control ◽  
Aided Diagnosis

The COVID-19 pandemic caused by the new coronavirus SARS-CoV-2 has changed the world as we know it. An early diagnosis is crucial in order to prevent new outbreaks and control its rapid spread. Medical imaging techniques, such as X-ray or chest computed tomography, are commonly used for this purpose due to their reliability for COVID-19 diagnosis. Computer-aided diagnosis systems could play an essential role in aiding radiologists in the screening process. In this work, a novel Deep Learning-based system, called COVID-XNet, is presented for COVID-19 diagnosis in chest X-ray images. The proposed system performs a set of preprocessing algorithms to the input images for variability reduction and contrast enhancement, which are then fed to a custom Convolutional Neural Network in order to extract relevant features and perform the classification between COVID-19 and normal cases. The system is trained and validated using a 5-fold cross-validation scheme, achieving an average accuracy of 94.43% and an AUC of 0.988. The output of the system can be visualized using Class Activation Maps, highlighting the main findings for COVID-19 in X-ray images. These promising results indicate that COVID-XNet could be used as a tool to aid radiologists and contribute to the fight against COVID-19.

scholarly journals Deep-learning convolutional neural networks with transfer learning accurately classify COVID-19 lung infection on portable chest radiographs

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10309
Author(s):  
Shreeja Kikkisetti ◽  
Jocelyn Zhu ◽  
Beiyi Shen ◽  
Haifang Li ◽  
Tim Q. Duong
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Bacterial Pneumonia ◽  
Lung Infection ◽  
Accurate Diagnosis ◽  
Viral Pneumonia ◽  
X Ray ◽  
Portable Chest ◽  
Chest X Ray ◽  
Lung Infections

Portable chest X-ray (pCXR) has become an indispensable tool in the management of Coronavirus Disease 2019 (COVID-19) lung infection. This study employed deep-learning convolutional neural networks to classify COVID-19 lung infections on pCXR from normal and related lung infections to potentially enable more timely and accurate diagnosis. This retrospect study employed deep-learning convolutional neural network (CNN) with transfer learning to classify based on pCXRs COVID-19 pneumonia (N = 455) on pCXR from normal (N = 532), bacterial pneumonia (N = 492), and non-COVID viral pneumonia (N = 552). The data was randomly split into 75% training and 25% testing, randomly. A five-fold cross-validation was used for the testing set separately. Performance was evaluated using receiver-operating curve analysis. Comparison was made with CNN operated on the whole pCXR and segmented lungs. CNN accurately classified COVID-19 pCXR from those of normal, bacterial pneumonia, and non-COVID-19 viral pneumonia patients in a multiclass model. The overall sensitivity, specificity, accuracy, and AUC were 0.79, 0.93, and 0.79, 0.85 respectively (whole pCXR), and were 0.91, 0.93, 0.88, and 0.89 (CXR of segmented lung). The performance was generally better using segmented lungs. Heatmaps showed that CNN accurately localized areas of hazy appearance, ground glass opacity and/or consolidation on the pCXR. Deep-learning convolutional neural network with transfer learning accurately classifies COVID-19 on portable chest X-ray against normal, bacterial pneumonia or non-COVID viral pneumonia. This approach has the potential to help radiologists and frontline physicians by providing more timely and accurate diagnosis.

scholarly journals Current Imaging Trends in COVID-19 Pneumonia

2020 ◽  
Vol 58 (226) ◽  
Author(s):  
Anamika Jha ◽  
Benu Lohani ◽  
Ram Kumar Ghimire
Keyword(s):  
Computed Tomography ◽  
Health Care ◽  
Disease Transmission ◽  
Medical Council ◽  
X Ray ◽  
System Functioning ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Tomography Scan

COVID-19 has rapidly emerged as a pandemic threatening lives and healthcare systems worldwide.With the emergence of the disease in Nepal, all faculties of medicine need to be well prepared toface the challenge. Fortunately, now plenty of research is available to facilitate our preparednessin the war against COVID-19. The reverse transcriptase-polymerase chain reaction is the currentgold standard diagnostic test and chest Computed Tomography scan for screening the disease isconsidered inappropriate by most society recommendations. The Nepal Radiologists’ Associationhas proposed its guidelines which have been endorsed by the Nepal Medical Council. This articleaims to summarize the role of imaging focusing on chest X-ray and Computed Tomography scanincluding the indications, specific findings, and important differentials. Imaging needs to be donetaking necessary precautions, to minimize disease transmission, protect health care personnel, andpreserve health care system functioning.

scholarly journals Recurrent spontaneous pneumothoraces and vaping in an 18-year-old man: a case report and review of the literature

2019 ◽  
Vol 13 (1) ◽  
Author(s):  
Alex Bonilla ◽  
Alexander J. Blair ◽  
Suliman M. Alamro ◽  
Rebecca A. Ward ◽  
Michael B. Feldman ◽  
...  
Keyword(s):  
Spontaneous Pneumothorax ◽  
Electronic Cigarettes ◽  
Diagnostic Testing ◽  
Cigarette Use ◽  
Case Presentation ◽  
X Ray ◽  
History Of ◽  
Chest X Ray ◽  
Tomography Scan

Abstract Background Primary spontaneous pneumothorax is a common disorder occurring in young adults without underlying lung disease. Although tobacco smoking is a well-documented risk factor for spontaneous pneumothorax, an association between electronic cigarette use (that is, vaping) and spontaneous pneumothorax has not been noted. We report a case of spontaneous pneumothoraces correlated with vaping. Case presentation An 18-year-old Caucasian man presented twice with recurrent right-sided spontaneous pneumothoraces within 2 weeks. He reported a history of vaping just prior to both episodes. Diagnostic testing was notable for a right-sided spontaneous pneumothorax on chest X-ray and computed tomography scan. His symptoms improved following insertion of a chest tube and drainage of air on each occasion. In the 2-week follow-up visit for the recurrent episode, he was asymptomatic and reported that he was no longer using electronic cigarettes. Conclusions Providers and patients should be aware of the potential risk of spontaneous pneumothorax associated with electronic cigarettes.

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