COVID-19 Diagnosis from Chest X-Ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super Resolution Convolutional Neural Network: Algorithm Development and Validation (Preprint)

2021 ◽  
Author(s):  
Happy Nkanta Monday ◽  
Jian Ping Li ◽  
Grace Ugochi Nneji ◽  
Md Altab Hossin ◽  
Rajesh Kumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Low Resolution ◽  
X Ray ◽  
Input Size ◽  
Multi Resolution Analysis ◽  
Chest X Ray ◽  
Improved Performance ◽  
High Level

BACKGROUND The chest x-ray (CXR) is becoming a useful method in the evaluation of coronavirus disease-19 (COVID-19). Despite the global COVID-19 uprising, utilizing computer-aided diagnosis approach for COVID-19 classification based on CXR images could significantly reduce clinician burden. There is no dispute that low resolution, noisy and irrelevant annotations in chest x-ray images is a major constraint to the performance of AI-based COVID-19 diagnosis. While few studies have made huge progress, they underestimate these bottlenecks. OBJECTIVE In this study, we propose a Super Resolution based Siamese Wavelet Multi-Resolution Convolutional Neural Network called COVID-SRWCNN for COVID-19 Classification using chest x-ray images. METHODS Concretely, we first reconstruct high-resolution (HR) counterparts from low resolution (LR) images of CXR images in order to enhance the quality of the dataset for improved performance of our model by proposing a novel enhanced fast super resolution convolutional neural network (EFSRCNN) to capture texture details in each given chest x-ray image. Since the datasets are collected from different sources with varying resolutions and the input layer of a convolutional neural network requires that the input size of the images in the training distribution must be fixed, therefore we extend the super resolution convolutional neural network by introducing an adaptive scaling operation to resize the images to a fixed resolution prior to the enhancement operation. Exploiting a mutual learning approach, the HR images are passed to the proposed siamese wavelet multi-resolution convolutional neural network to learn the high-level features for COVID-19 classification. RESULTS We validate the proposed COVID-SRWCNN model on public-source datasets achieving an accuracy of 99.6%, precision of 99.7%, and F1 score of 99.9%. Our screening technique achieved 99.8 % AUC, 99.7% sensitivity and 99.6% specificity. CONCLUSIONS Owing to the fact that COVID-19 chest x-ray dataset are low in quality, experimental results show that our proposed algorithm obtained up-to-date performance which is useful for COVID-19 screening.

scholarly journals MFBCNNC: Momentum factor biogeography convolutional neural network for COVID-19 detection via chest X-ray images

2021 ◽  
Vol 232 ◽  
pp. 107494
Author(s):  
Junding Sun ◽  
Xiang Li ◽  
Chaosheng Tang ◽  
Shui-Hua Wang ◽  
Yu-Dong Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
X Ray ◽  
Chest X Ray

Automated detection of pneumonia cases using deep transfer learning with paediatric chest X-ray images

2021 ◽  
pp. 20201263
Author(s):  
Mohammad Salehi ◽  
Reza Mohammadi ◽  
Hamed Ghaffari ◽  
Nahid Sadighi ◽  
Reza Reiazi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Area Under Curve ◽  
Binary Classification ◽  
Classification Performance ◽  
Learning Approach ◽  
Insufficient Data ◽  
X Ray ◽  
Chest X Ray

Objective: Pneumonia is a lung infection and causes the inflammation of the small air sacs (Alveoli) in one or both lungs. Proper and faster diagnosis of pneumonia at an early stage is imperative for optimal patient care. Currently, chest X-ray is considered as the best imaging modality for diagnosing pneumonia. However, the interpretation of chest X-ray images is challenging. To this end, we aimed to use an automated convolutional neural network-based transfer-learning approach to detect pneumonia in paediatric chest radiographs. Methods: Herein, an automated convolutional neural network-based transfer-learning approach using four different pre-trained models (i.e. VGG19, DenseNet121, Xception, and ResNet50) was applied to detect pneumonia in children (1–5 years) chest X-ray images. The performance of different proposed models for testing data set was evaluated using five performances metrics, including accuracy, sensitivity/recall, Precision, area under curve, and F1 score. Results: All proposed models provide accuracy greater than 83.0% for binary classification. The pre-trained DenseNet121 model provides the highest classification performance of automated pneumonia classification with 86.8% accuracy, followed by Xception model with an accuracy of 86.0%. The sensitivity of the proposed models was greater than 91.0%. The Xception and DenseNet121 models achieve the highest classification performance with F1-score greater than 89.0%. The plotted area under curve of receiver operating characteristics of VGG19, Xception, ResNet50, and DenseNet121 models are 0.78, 0.81, 0.81, and 0.86, respectively. Conclusion: Our data showed that the proposed models achieve a high accuracy for binary classification. Transfer learning was used to accelerate training of the proposed models and resolve the problem associated with insufficient data. We hope that these proposed models can help radiologists for a quick diagnosis of pneumonia at radiology departments. Moreover, our proposed models may be useful to detect other chest-related diseases such as novel Coronavirus 2019. Advances in knowledge: Herein, we used transfer learning as a machine learning approach to accelerate training of the proposed models and resolve the problem associated with insufficient data. Our proposed models achieved accuracy greater than 83.0% for binary classification.

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