Classification of Chest X-Ray Images Using Novel Adaptive Morphological Neural Networks

This paper presents an approach for the classification of child chest X-ray images into two classes: pneumonia and normal. We employ Convolutional Neural Networks, from pre-trained networks together with a quantization process, using the platform TensorFlow Lite method. This reduces the processing requirement and computational cost. Results have shown accuracy up to 95.4% and 94.2% for MobileNetV1 and MobileNetV2, respectively. The resulting mobile app also presents a simple and intuitive user interface.

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Classification of x-ray images for detection of childhood pneumonia using pre-trained neural networks

Revista Brasileira de Computação Aplicada ◽

10.5335/rbca.v12i3.10343 ◽

2020 ◽

Vol 12 (3) ◽

pp. 132-141

Author(s):

Nator Junior Carvalho da Costa ◽

Jose Vigno Moura Sousa ◽

Domingos Bruno Sousa Santos ◽

Francisco das Chagas Fontenele Marques Junior ◽

Rodrigo Teixeira de Melo

Keyword(s):

Neural Networks ◽

Bacterial Pneumonia ◽

Good Accuracy ◽

Viral Pneumonia ◽

Data Set ◽

X Ray ◽

Chest X Ray ◽

And Training ◽

Validation Testing

This paper describes a comparison between three pre-trained neural networks for the classification of chest X-ray images: Xception, Inception V3, and NasNetLarge. Networks were implemented using learning transfer; The database used was the chest x-ray data set, which contains a total of 5856 chest x-ray images of pediatric patients aged one to five years, with three classes: Normal Viral Pneumonia and Bacterial Pneumonia. Data were divided into three groups: validation, testing and training. A comparison was made with the work of kermany who implemented the Inception V3 network in two ways: (Pneumonia X Normal) and (Bacterial Pneumonia X Viral Pneumonia). The nets used had good accuracy, being the NasNetLarge network the best precision, which was 95.35 \% (Pneumonia X Normal) and 91.79 \% (Viral Pneumonia X Bacterial Pneumonia) against 92.80 \% in (Pneumonia X Normal) and 90.70 \% (Viral Pneumonia X Bacterial Pneumonia) from kermany's work, the Xception network also achieved an improvement in accuracy compared to kermany's work, with 93.59 \% at (Normal X Pneumonia) and 91.03 \% in (Viral Pneumonia X Bacterial Pneumonia).

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COVID-19 Classification of X-ray Images Using Deep Neural Networks

10.1101/2020.10.01.20204073 ◽

2020 ◽

Author(s):

Elisha Goldstein ◽

Daphna Keidar ◽

Daniel Yaron ◽

Yair Shachar ◽

Ayelet Blass ◽

...

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Receiver Operating Characteristic ◽

Deep Neural Networks ◽

Operating Characteristic ◽

Learning Model ◽

X Ray ◽

Machine Learning Model ◽

Chest X Ray

AbstractBackgroundIn the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in the diagnosis and monitoring of patients with COVID-19. Machine learning solutions have been shown to be useful for X-ray analysis and classification in a range of medical contexts.PurposeThe purpose of this study is to create and evaluate a machine learning model for diagnosis of COVID-19, and to provide a tool for searching for similar patients according to their X-ray scans.Materials and MethodsIn this retrospective study, a classifier was built using a pre-trained deep learning model (ReNet50) and enhanced by data augmentation and lung segmentation to detect COVID-19 in frontal CXR images collected between January 2018 and July 2020 in four hospitals in Israel. A nearest-neighbors algorithm was implemented based on the network results that identifies the images most similar to a given image. The model was evaluated using accuracy, sensitivity, area under the curve (AUC) of receiver operating characteristic (ROC) curve and of the precision-recall (P-R) curve.ResultsThe dataset sourced for this study includes 2362 CXRs, balanced for positive and negative COVID-19, from 1384 patients (63 +/- 18 years, 552 men). Our model achieved 89.7% (314/350) accuracy and 87.1% (156/179) sensitivity in classification of COVID-19 on a test dataset comprising 15% (350 of 2326) of the original data, with AUC of ROC 0.95 and AUC of the P-R curve 0.94. For each image we retrieve images with the most similar DNN-based image embeddings; these can be used to compare with previous cases.ConclusionDeep Neural Networks can be used to reliably classify CXR images as COVID-19 positive or negative. Moreover, the image embeddings learned by the network can be used to retrieve images with similar lung findings.SummaryDeep Neural Networks and can be used to reliably predict chest X-ray images as positive for coronavirus disease 2019 (COVID-19) or as negative for COVID-19.Key ResultsA machine learning model was able to detect chest X-ray (CXR) images of patients tested positive for coronavirus disease 2019 with accuracy of 89.7%, sensitivity of 87.1% and area under receiver operating characteristic curve of 0.95.A tool was created for finding existing CXR images with imaging characteristics most similar to a given CXR, according to the model’s image embeddings.

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Deep chest X‐ray : Detection and classification of lesions based on deep convolutional neural networks

International Journal of Imaging Systems and Technology ◽

10.1002/ima.22508 ◽

2020 ◽

Author(s):

Yongwon Cho ◽

Sang Min Lee ◽

Young‐Hoon Cho ◽

June‐Goo Lee ◽

Beomhee Park ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Deep Convolutional Neural Networks ◽

X Ray ◽

Chest X Ray

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Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Networks

10.1101/2020.05.01.20088211 ◽

2020 ◽

Cited By ~ 4

Author(s):

Sohaib Asif ◽

Yi Wenhui ◽

Hou Jin ◽

Yi Tao ◽

Si Jinhai

Keyword(s):

Neural Networks ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Well Being ◽

Deep Convolutional Neural Networks ◽

X Ray ◽

Digital Chest ◽

Normal Chest ◽

Chest X Ray

AbstractThe COVID-19 pandemic continues to have a devastating effect on the health and well-being of the global population. A vital step in the combat towards COVID-19 is a successful screening of contaminated patients, with one of the key screening approaches being radiological imaging using chest radiography. This study aimed to automatically detect COVID‐ 19 pneumonia patients using digital chest x‐ ray images while maximizing the accuracy in detection using deep convolutional neural networks (DCNN). The dataset consists of 864 COVID‐ 19, 1345 viral pneumonia and 1341 normal chest x‐ ray images. In this study, DCNN based model Inception V3 with transfer learning have been proposed for the detection of coronavirus pneumonia infected patients using chest X-ray radiographs and gives a classification accuracy of more than 98% (training accuracy of 97% and validation accuracy of 93%). The results demonstrate that transfer learning proved to be effective, showed robust performance and easily deployable approach for COVID-19 detection.

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Chest X-Ray Classification of Lung Diseases Using Deep Learning

Green Intelligent Systems and Applications ◽

10.53623/gisa.v1i1.32 ◽

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.

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