Viral and Bacterial Pneumonia Diagnosis via Deep Learning Techniques and Model Explainability

Nguyen, Hai Thanh; Bao, Toan; Hoang, Huong; Phuoc, Trung; Cong, Nghi

doi:10.14569/ijacsa.2020.0110780

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…This performance is quite good considering its high performance with a low computing load. The evaluation results of UBNet v2 with a total of 7 layers, as shown in Table 10, can match the performance of other models such as those developed by Nguyen et al [17], Residual, Inception, dan VGG19 [5].…”

Section: Cnn Architecture For Pneumonia and Normal Image Classificationsupporting

confidence: 57%

UBNet: Deep learning-based approach for automatic X-ray image detection of pneumonia and COVID-19 patients

Widodo

Naba

Mahasin

et al. 2022

XST

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BACKGROUND: Analysis of chest X-ray images is one of the primary standards in diagnosing patients with COVID-19 and pneumonia, which is faster than using PCR Swab method. However, accuracy of using X-ray images needs to be improved. OBJECTIVE: To develop a new deep learning system of chest X-ray images and evaluate whether it can quickly and accurately detect pneumonia and COVID-19 patients. METHODS: The developed deep learning system (UBNet v3) uses three architectural hierarchies, namely first, to build an architecture containing 7 convolution layers and 3 ANN layers (UBNet v1) to classify between normal images and pneumonia images. Second, using 4 layers of convolution and 3 layers of ANN (UBNet v2) to classify between bacterial and viral pneumonia images. Third, using UBNet v1 to classify between pneumonia virus images and COVID-19 virus infected images. An open-source database with 9,250 chest X-ray images including 3,592 COVID-19 images were used in this study to train and test the developed deep learning models. RESULTS: CNN architecture with a hierarchical scheme developed in UBNet v3 using a simple architecture yielded following performance indices to detect chest X-ray images of COVID-19 patients namely, 99.6%accuracy, 99.7%precision, 99.7%sensitivity, 99.1%specificity, and F1 score of 99.74%. A desktop GUI-based monitoring and classification system supported by a simple CNN architecture can process each chest X-ray image to detect and classify COVID-19 image with an average time of 1.21 seconds. CONCLUSION: Using three hierarchical architectures in UBNet v3 improves system performance in classifying chest X-ray images of pneumonia and COVID-19 patients. A simple architecture also speeds up image processing time.

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Section: Cnn Architecture For Pneumonia and Normal Image Classificationsupporting

confidence: 57%

UBNet: Deep learning-based approach for automatic X-ray image detection of pneumonia and COVID-19 patients

Widodo

Naba

Mahasin

et al. 2022

XST

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“…In addition to pre-training models with general image datasets such as ImageNet (which is commonly employed in many algorithms in medical imaging [ 63 ]), pre-training models with large datasets of adult chest radiographs provide a promising approach to reducing the number of pediatric chest radiographs required for training [ 37 , 54 ]. Tang et al [ 54 ] used GoogleNet (Inception v3) to classify pediatric chest radiographs from the GWCMC dataset as pneumonia present vs. absent.…”

Section: Resultsmentioning

confidence: 99%

Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review

et al. 2022

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“…In [20] proposed a method for diagnosis based on an imaging study of patients with pneumonia by means of deep learning techniques, in order to achieve a distinction between patients with pneumonia and healthy patients, as well as to differentiate viral between and bacterial pneumonia. The model achieved acceptable results, but with certain limitations in the classification of viral and bacterial pneumonia.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Mobile Application based on the Convolutional Neural Network for the Diagnosis of Pneumonia

Flores-Rodriguez¹,

Cabanillas-Carbonell²

2022

IJACSA

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Pneumonia is the main cause of infant mortality in Peru, which has led to plansfig, such as vaccination campaigns, greater economic investment in health, and the strengthening of specialized medical personnel, however, mortality rates remain high. In this sense, the implementation of new computer technologies such as Deep Learning through the use of the artificial neural network is proposed. The objective of this project was to determine the influence of a mobile application based on a Convolutional Neural Network for the diagnosis of Pneumonia, the project consists of the analysis of images of Chest X-rays with Pneumonia and Normal by means of an application developed called "Diagnost". The study was carried out considering a control group and a study group formed by 33 medical staff members who used the application. The analysis of the data obtained was made based on the study of 3 indicators, detection time, result in accuracy, and reduction of medical assistance. According to the results, it was concluded that the mobile application based on the convolutional neural network allows the early detection of Pneumonia and allows the reduction of medical assistance, however, it is still necessary to continue working on the accuracy of the diagnosis.

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Viral and Bacterial Pneumonia Diagnosis via Deep Learning Techniques and Model Explainability

Cited by 5 publications

References 18 publications

UBNet: Deep learning-based approach for automatic X-ray image detection of pneumonia and COVID-19 patients

UBNet: Deep learning-based approach for automatic X-ray image detection of pneumonia and COVID-19 patients

Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review

Implementation of a Mobile Application based on the Convolutional Neural Network for the Diagnosis of Pneumonia

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