scholarly journals Ensemble of EfficientNets for the Diagnosis of Tuberculosis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Mustapha Oloko-Oba ◽  
Serestina Viriri
Keyword(s):  
Rural Communities ◽  
Fine Tuning ◽  
Diagnostic Systems ◽  
Developing Regions ◽  
X Ray ◽  
Life Threatening ◽  
Causes Of Mortality ◽  
Chest X Ray ◽  
Severity Of The Disease

Tuberculosis (TB) remains a life-threatening disease and is one of the leading causes of mortality in developing regions due to poverty and inadequate medical resources. Tuberculosis is medicable, but it necessitates early diagnosis through reliable screening techniques. Chest X-ray is a recommended screening procedure for identifying pulmonary abnormalities. Still, this recommendation is not enough without experienced radiologists to interpret the screening results, which forms part of the problems in rural communities. Consequently, various computer-aided diagnostic systems have been developed for the automatic detection of tuberculosis. However, their sensitivity and accuracy are still significant challenges that require constant improvement due to the severity of the disease. Hence, this study explores the application of a leading state-of-the-art convolutional neural network (EfficientNets) model for the classification of tuberculosis. Precisely, five variants of EfficientNets were fine-tuned and implemented on two prominent and publicly available chest X-ray datasets (Montgomery and Shenzhen). The experiments performed show that EfficientNet-B4 achieved the best accuracy of 92.33% and 94.35% on both datasets. These results were then improved through Ensemble learning and reached 97.44%. The performance recorded in this study portrays the efficiency of fine-tuning EfficientNets on medical imaging classification through Ensemble.

scholarly journals Detection of COVID-19 Using Transfer Learning and Grad-CAM Visualization on Indigenously Collected X-ray Dataset

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5813
Author(s):  
Muhammad Umair ◽  
Muhammad Shahbaz Khan ◽  
Fawad Ahmed ◽  
Fatmah Baothman ◽  
Fehaid Alqahtani ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Input Image ◽  
Fine Tuning ◽  
Feature Maps ◽  
Virus Family ◽  
X Ray ◽  
Normal Chest ◽  
Chest X Ray ◽  
Confusion Matrices

The COVID-19 outbreak began in December 2019 and has dreadfully affected our lives since then. More than three million lives have been engulfed by this newest member of the corona virus family. With the emergence of continuously mutating variants of this virus, it is still indispensable to successfully diagnose the virus at early stages. Although the primary technique for the diagnosis is the PCR test, the non-contact methods utilizing the chest radiographs and CT scans are always preferred. Artificial intelligence, in this regard, plays an essential role in the early and accurate detection of COVID-19 using pulmonary images. In this research, a transfer learning technique with fine tuning was utilized for the detection and classification of COVID-19. Four pre-trained models i.e., VGG16, DenseNet-121, ResNet-50, and MobileNet were used. The aforementioned deep neural networks were trained using the dataset (available on Kaggle) of 7232 (COVID-19 and normal) chest X-ray images. An indigenous dataset of 450 chest X-ray images of Pakistani patients was collected and used for testing and prediction purposes. Various important parameters, e.g., recall, specificity, F1-score, precision, loss graphs, and confusion matrices were calculated to validate the accuracy of the models. The achieved accuracies of VGG16, ResNet-50, DenseNet-121, and MobileNet are 83.27%, 92.48%, 96.49%, and 96.48%, respectively. In order to display feature maps that depict the decomposition process of an input image into various filters, a visualization of the intermediate activations is performed. Finally, the Grad-CAM technique was applied to create class-specific heatmap images in order to highlight the features extracted in the X-ray images. Various optimizers were used for error minimization purposes. DenseNet-121 outperformed the other three models in terms of both accuracy and prediction.

scholarly journals Novel Multi-Modal Throat Inflammation and Chest Radiography based Early-Diagnosis and Mass-Screening of COVID-19

2021 ◽  
Vol 15 (1) ◽  
pp. 226-235
Author(s):  
Ojas A. Ramwala ◽  
Poojan Dalal ◽  
Parima Parikh ◽  
Upena Dalal ◽  
Mita C. Paunwala ◽  
...  
Keyword(s):  
Mass Screening ◽  
Chest Radiography ◽  
Fine Tuning ◽  
Rt Pcr ◽  
Close Collaboration ◽  
X Ray ◽  
Non Invasive ◽  
Life Threatening ◽  
Chest X Ray ◽  
Future Direction

Background: The upsurge of COVID-19 has received significant international contemplation considering its life-threatening ramifications. To ensure that the susceptible patients can be quarantined to control the spread of the disease during the incubation period of the coronavirus, it becomes imperative to automatically and non-invasively mass screen patients. The diagnosis using RT-PCR is arduous and time-consuming. Currently, the non-invasive mass screening of susceptible cases is being performed by utilizing the thermal screening technique. However, with the consumption of paracetamol, the symptoms of fever can be suppressed. Methods: A novel multi-modal approach has been proposed. Throat inflammation-based mass screening and early prediction followed by Chest X-Ray based diagnosis have been proposed. Depth-wise separable convolutions have been utilized by fine-tuning Xception Net and Mobile Net architectures. NADAM optimizer has been leveraged to promote faster convergence. Results: The proposed method achieved 91% accuracy on the throat inflammation identification task and 96% accuracy on chest radiography conducted on the dataset. Conclusion: Evaluation of the proposed method indicates promising results and henceforth validates its clinical reliability. The future direction could be working on a larger dataset in close collaboration with the medical fraternity.

scholarly journals COVID-19 diagnosis from chest X-ray images using transfer learning: Enhanced performance by debiasing dataloader

2021 ◽  
Vol 29 (1) ◽  
pp. 19-36
Author(s):  
Çağín Polat ◽  
Onur Karaman ◽  
Ceren Karaman ◽  
Güney Korkmaz ◽  
Mehmet Can Balcı ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Critical Time ◽  
Image Data ◽  
Mapping Method ◽  
Fine Tuning ◽  
Learning Stage ◽  
X Ray ◽  
Testing Dataset ◽  
X Ray Imaging ◽  
Chest X Ray

BACKGROUND: Chest X-ray imaging has been proved as a powerful diagnostic method to detect and diagnose COVID-19 cases due to its easy accessibility, lower cost and rapid imaging time. OBJECTIVE: This study aims to improve efficacy of screening COVID-19 infected patients using chest X-ray images with the help of a developed deep convolutional neural network model (CNN) entitled nCoV-NET. METHODS: To train and to evaluate the performance of the developed model, three datasets were collected from resources of “ChestX-ray14”, “COVID-19 image data collection”, and “Chest X-ray collection from Indiana University,” respectively. Overall, 299 COVID-19 pneumonia cases and 1,522 non-COVID 19 cases are involved in this study. To overcome the probable bias due to the unbalanced cases in two classes of the datasets, ResNet, DenseNet, and VGG architectures were re-trained in the fine-tuning stage of the process to distinguish COVID-19 classes using a transfer learning method. Lastly, the optimized final nCoV-NET model was applied to the testing dataset to verify the performance of the proposed model. RESULTS: Although the performance parameters of all re-trained architectures were determined close to each other, the final nCOV-NET model optimized by using DenseNet-161 architecture in the transfer learning stage exhibits the highest performance for classification of COVID-19 cases with the accuracy of 97.1 %. The Activation Mapping method was used to create activation maps that highlights the crucial areas of the radiograph to improve causality and intelligibility. CONCLUSION: This study demonstrated that the proposed CNN model called nCoV-NET can be utilized for reliably detecting COVID-19 cases using chest X-ray images to accelerate the triaging and save critical time for disease control as well as assisting the radiologist to validate their initial diagnosis.

scholarly journals Diagnosing gastric volvulus in chest X-ray: report of three cases

2020 ◽  
Vol 2020 (7) ◽  
Author(s):  
Narendra Pandit ◽  
Abhijeet Kumar ◽  
Tek Narayan Yadav ◽  
Qamar Alam Irfan ◽  
Sujan Gautam ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Acute Abdomen ◽  
Gastric Volvulus ◽  
X Ray ◽  
Pain Abdomen ◽  
Life Threatening ◽  
Chest X Ray ◽  
High Index Of Suspicion ◽  
Specific Symptoms ◽  
Prompt Diagnosis

Abstract Gastric volvulus is a rare abnormal rotation of the stomach along its axis. It is a surgical emergency, hence requires prompt diagnosis and treatment to prevent life-threatening gangrenous changes. Hence, a high index of suspicion is required in any patients presenting with an acute abdomen in emergency. The entity can present acutely with pain abdomen and vomiting, or as chronic with non-specific symptoms. Chest X-ray findings to diagnose it may be overlooked in patients with acute abdomen. Here, we report three patients with gastric volvulus, where the diagnosis was based on the chest X-ray findings, confirmed with computed tomography, and managed successfully with surgery.

Detection of COVID-19 cases through X-ray images using hybrid deep neural network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajit Nair ◽  
Santosh Vishwakarma ◽  
Mukesh Soni ◽  
Tejas Patel ◽  
Shubham Joshi
Keyword(s):  
Real Time ◽  
Binary Classification ◽  
Data Set ◽  
Content Type ◽  
X Ray ◽  
Internal Parameters ◽  
Average Accuracy ◽  
Chest X Ray ◽  
Multi Class Classification

Purpose The latest 2019 coronavirus (COVID-2019), which first appeared in December 2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It has had a devastating impact on daily lives, the public's health and the global economy. The positive cases must be identified as soon as possible to avoid further dissemination of this disease and swift care of patients affected. The need for supportive diagnostic instruments increased, as no specific automated toolkits are available. The latest results from radiology imaging techniques indicate that these photos provide valuable details on the virus COVID-19. User advanced artificial intelligence (AI) technologies and radiological imagery can help diagnose this condition accurately and help resolve the lack of specialist doctors in isolated areas. In this research, a new paradigm for automatic detection of COVID-19 with bare chest X-ray images is displayed. Images are presented. The proposed model DarkCovidNet is designed to provide correct binary classification diagnostics (COVID vs no detection) and multi-class (COVID vs no results vs pneumonia) classification. The implemented model computed the average precision for the binary and multi-class classification of 98.46% and 91.352%, respectively, and an average accuracy of 98.97% and 87.868%. The DarkNet model was used in this research as a classifier for a real-time object detection method only once. A total of 17 convolutionary layers and different filters on each layer have been implemented. This platform can be used by the radiologists to verify their initial application screening and can also be used for screening patients through the cloud. Design/methodology/approach This study also uses the CNN-based model named Darknet-19 model, and this model will act as a platform for the real-time object detection system. The architecture of this system is designed in such a way that they can be able to detect real-time objects. This study has developed the DarkCovidNet model based on Darknet architecture with few layers and filters. So before discussing the DarkCovidNet model, look at the concept of Darknet architecture with their functionality. Typically, the DarkNet architecture consists of 5 pool layers though the max pool and 19 convolution layers. Assume as a convolution layer, and as a pooling layer. Findings The work discussed in this paper is used to diagnose the various radiology images and to develop a model that can accurately predict or classify the disease. The data set used in this work is the images bases on COVID-19 and non-COVID-19 taken from the various sources. The deep learning model named DarkCovidNet is applied to the data set, and these have shown signification performance in the case of binary classification and multi-class classification. During the multi-class classification, the model has shown an average accuracy 98.97% for the detection of COVID-19, whereas in a multi-class classification model has achieved an average accuracy of 87.868% during the classification of COVID-19, no detection and Pneumonia. Research limitations/implications One of the significant limitations of this work is that a limited number of chest X-ray images were used. It is observed that patients related to COVID-19 are increasing rapidly. In the future, the model on the larger data set which can be generated from the local hospitals will be implemented, and how the model is performing on the same will be checked. Originality/value Deep learning technology has made significant changes in the field of AI by generating good results, especially in pattern recognition. A conventional CNN structure includes a convolution layer that extracts characteristics from the input using the filters it applies, a pooling layer that reduces calculation efficiency and the neural network's completely connected layer. A CNN model is created by integrating one or more of these layers, and its internal parameters are modified to accomplish a specific mission, such as classification or object recognition. A typical CNN structure has a convolution layer that extracts features from the input with the filters it applies, a pooling layer to reduce the size for computational performance and a fully connected layer, which is a neural network. A CNN model is created by combining one or more such layers, and its internal parameters are adjusted to accomplish a particular task, such as classification or object recognition.

Life-threatening presentation of a parahiatal hernia after esophagectomy: a case report and review of the literature

2021 ◽  
Vol 14 (6) ◽  
pp. e242158
Author(s):  
Camille Plourde ◽  
Émilie Comeau
Keyword(s):  
Small Bowel ◽  
Transverse Colon ◽  
Acute Abdominal Pain ◽  
Haemodynamic Instability ◽  
Acute Setting ◽  
X Ray ◽  
Left Diaphragm ◽  
Life Threatening ◽  
Chest X Ray ◽  
Obstructive Shock

A woman presented to our hospital with acute abdominal pain 7 months following an oesophagectomy. A chest X-ray revealed a new elevation of the left diaphragm. CT demonstrated a large left diaphragmatic hernia incarcerated with non-enhancing transverse colon and loops of small bowel. She deteriorated rapidly into obstructive shock and was urgently brought to the operating room for a laparotomy. The diaphragmatic orifice was identified in a left parahiatal position, consistent with a parahiatal hernia. Incarcerated necrotic transverse colon and ischaemic loops of small bowel were resected, and the diaphragmatic defect was closed primarily. Because of haemodynamic instability, the abdomen was temporarily closed, and a second look was performed 24 hours later, allowing anastomosis and definitive closure. Parahiatal hernias are rare complications following surgical procedures and can lead to devastating life-threatening complications, such as an obstructive shock. Expeditious diagnosis and management are required in the acute setting.

scholarly journals A dangerous pleural effusion

2010 ◽  
Vol 92 (5) ◽  
pp. e53-e54 ◽  
Author(s):  
Somprakas Basu ◽  
Shilpi Bhadani ◽  
Vijay K Shukla
Keyword(s):  
Rare Complication ◽  
Sudden Onset ◽  
Biliary Surgery ◽  
Biliary Leak ◽  
X Ray ◽  
Delay In Diagnosis ◽  
Aged Woman ◽  
Middle Aged Woman ◽  
Life Threatening ◽  
Chest X Ray

Bilothorax is a rare complication of biliary peritonitis and, if not treated promptly, can be life-threatening. We report a case of a middle-aged woman who had undergone a bilio-enteric bypass and subsequently a biliary leak developed, which finally led to intra-abdominal biliary collection and spontaneous bilothorax. The clinical course was rapid and mimicked venous thromboembolism, myocardial infarction and pulmonary oedema, which led to a delay in diagnosis and management and finally death. We high-light the fact that bilothorax, although a rare complication of biliary surgery, should always be considered as a probable cause of massive effusion and sudden-onset respiratory and cardiovascular collapse in the postoperative period. A chest X-ray and a diagnostic pleural tap can confirm the diagnosis. Once detected, an aggressive management should be instituted to prevent organ failure and death.

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