CHEST X-RAY IMAGE CLASSIFICATION USING FASTER R-CNN

Chest x-ray image analysis is the common medical imaging exam needed to assess different pathologies. Having an automated solution for the analysis can contribute to minimizing the workloads, improve efficiency and reduce the potential of reading errors. Many methods have been proposed to address chest x-ray image classification and detection. However, the application of regional-based convolutional neural networks (CNN) is currently limited. Thus, we propose an approach to classify chest x-ray images into either one of two categories, pathological or normal based on Faster Regional-CNN model. This model utilizes Region Proposal Network (RPN) to generate region proposals and perform image classification. By applying this model, we can potentially achieve two key goals, high confidence in the classification and reducing the computation time. The results show the applied model achieved higher accuracy as compared to the medical representatives on the random chest x-ray images. The classification model is also reasonably effective in classifying between finding and normal chest x-ray image captured through a live webcam.

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4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection

10.1101/2020.06.22.20137547 ◽

2020 ◽

Cited By ~ 1

Author(s):

Asmaa Abbas ◽

Mohammed M. Abdelsamea ◽

Mohamed Medhat Gaber

Keyword(s):

Image Classification ◽

Transfer Learning ◽

Large Scale ◽

Learning Strategy ◽

Classification Model ◽

Decomposition Approach ◽

X Ray ◽

Sample Decomposition ◽

Chest X Ray ◽

Coarse To Fine

ABSTRACTDue to the high availability of large-scale annotated image datasets, knowledge transfer from pre-trained models showed outstanding performance in medical image classification. However, building a robust image classification model for datasets with data irregularity or imbalanced classes can be a very challenging task, especially in the medical imaging domain. In this paper, we propose a novel deep convolutional neural network, we called Self Supervised Super Sample Decomposition for Transfer learning (4S-DT) model. 4S-DT encourages a coarse-to-fine transfer learning from large-scale image recognition tasks to a specific chest X-ray image classification task using a generic self-supervised sample decomposition approach. Our main contribution is a novel self-supervised learning mechanism guided by a super sample decomposition of unlabelled chest X-ray images. 4S-DT helps in improving the robustness of knowledge transformation via a downstream learning strategy with a class-decomposition layer to simplify the local structure of the data. 4S-DT can deal with any irregularities in the image dataset by investigating its class boundaries using a downstream class-decomposition mechanism. We used 50,000 unlabelled chest X-ray images to achieve our coarse-to-fine transfer learning with an application to COVID-19 detection, as an exemplar. 4S-DT has achieved an accuracy of 97.54% in the detection of COVID-19 cases on an extended test set enriched by augmented images, out of which all real COVID-19 cases were detected, which was the highest accuracy obtained when compared to other methods.

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Chest X-Ray Image Classification on Common Thorax Diseases using GLCM and AlexNet Deep Features

International Journal of Integrated Engineering ◽

10.30880/ijie.2019.11.04.003 ◽

2019 ◽

Vol 11 (4) ◽

Author(s):

Tengku Afiah Mardhiah Tengku Zainul Akmal ◽

Joel Chia Ming Than ◽

Haslailee Abdullah ◽

Norliza Mohd Noor

Keyword(s):

Image Classification ◽

X Ray ◽

Chest X Ray

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Performance Comparison for Different Neural Network Architectures for chest X-Ray Image Classification

2021 7th International Conference on Engineering, Applied Sciences and Technology (ICEAST) ◽

10.1109/iceast52143.2021.9426289 ◽

2021 ◽

Author(s):

Pinyada Rajadanuraks ◽

Sarapom Suranuntchai ◽

Suejit Pechprasam ◽

Treesukon Treebupachatsakul

Keyword(s):

Neural Network ◽

Image Classification ◽

Performance Comparison ◽

Network Architectures ◽

X Ray ◽

Chest X Ray ◽

Neural Network Architectures

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

New Generation Computing ◽

10.1007/s00354-021-00121-7 ◽

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.

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A normal chest X-ray does not rule out the patient’s version

Archivos de Bronconeumología (English Edition) ◽

10.1016/j.arbr.2021.07.002 ◽

2021 ◽

Author(s):

Elena Forcén ◽

María José Bernabé ◽

Roberto Larrosa-Barrero

Keyword(s):

X Ray ◽

Normal Chest ◽

Chest X Ray ◽

Rule Out

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Reliable Learning with PDE-Based CNNs and DenseNets for Detecting COVID-19, Pneumonia, and Tuberculosis from Chest X-Ray Images

Mathematics ◽

10.3390/math9040434 ◽

2021 ◽

Vol 9 (4) ◽

pp. 434

Author(s):

Anca Nicoleta Marginean ◽

Delia Doris Muntean ◽

George Adrian Muntean ◽

Adelina Priscu ◽

Adrian Groza ◽

...

Keyword(s):

Transfer Learning ◽

Negative Impact ◽

X Ray ◽

Domain Specific ◽

Direct Training ◽

Public Data ◽

Normal Chest ◽

Chest X Ray ◽

The Stability ◽

Definition Of

It has recently been shown that the interpretation by partial differential equations (PDEs) of a class of convolutional neural networks (CNNs) supports definition of architectures such as parabolic and hyperbolic networks. These networks have provable properties regarding the stability against the perturbations of the input features. Aiming for robustness, we tackle the problem of detecting changes in chest X-ray images that may be suggestive of COVID-19 with parabolic and hyperbolic CNNs and with domain-specific transfer learning. To this end, we compile public data on patients diagnosed with COVID-19, pneumonia, and tuberculosis, along with normal chest X-ray images. The negative impact of the small number of COVID-19 images is reduced by applying transfer learning in several ways. For the parabolic and hyperbolic networks, we pretrain the networks on normal and pneumonia images and further use the obtained weights as the initializers for the networks to discriminate between COVID-19, pneumonia, tuberculosis, and normal aspects. For DenseNets, we apply transfer learning twice. First, the ImageNet pretrained weights are used to train on the CheXpert dataset, which includes 14 common radiological observations (e.g., lung opacity, cardiomegaly, fracture, support devices). Then, the weights are used to initialize the network which detects COVID-19 and the three other classes. The resulting networks are compared in terms of how well they adapt to the small number of COVID-19 images. According to our quantitative and qualitative analysis, the resulting networks are more reliable compared to those obtained by direct training on the targeted dataset.

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The normal chest X-ray

Radiology for Medical Finals ◽

10.1201/9781315155395-3 ◽

2017 ◽

pp. 15-34

Author(s):

Thomas Kurka

Keyword(s):

X Ray ◽

Normal Chest ◽

Chest X Ray

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

Sensors ◽

10.3390/s21175813 ◽

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.

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Subcutaneous Emphysema in Pregnancy

Journal of Nepal Medical Association ◽

10.31729/jnma.39 ◽

2011 ◽

Vol 51 (183) ◽

Author(s):

A Shrestha ◽

S Acharya

Keyword(s):

Subcutaneous Emphysema ◽

Late Pregnancy ◽

X Ray ◽

Close Observation ◽

The Face ◽

Normal Chest ◽

Chest X Ray ◽

Keywords Pregnancy ◽

Systemic Examination ◽

The Right

Spontaneous pneumomediastinum and subcutaneous emphysema are rare complications of labor, especially in the late pregnancy period, but they are usually self-limiting. Management includes avoidance of exacerbative factors and close observation with supportive treatment. A 19-year-old primi gravida at 36 weeks pregnancy presented with swelling over the right side of the face, neck and chest. Her general examination was normal. Systemic examination revealed swelling with palpatory crepitation over the right side of chest, neck and face, and other examination findings were normal. Chest X-ray revealed subcutaneous emphysema without pneumothorax. The patient left hospital against medical advice. Keywords: Pregnancy; subcutaneous emphysema; pneumomediastinum.

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