Chest X-Ray Image Analysis of Convolutional Neural Network Models with Transfer Learning for Prediction of COVID Patients

2021 ◽  
pp. 353-361
Author(s):  
M. Shyamala Devi ◽  
P. Swathi ◽  
N. Pavan Kumar ◽  
Ravi Varma Tungala ◽  
Saranya Vivekanandan ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Network Models ◽  
Neural Network Models ◽  
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.

scholarly journals Convolutional Neural Network for Remote-Sensing Scene Classification: Transfer Learning Analysis

2019 ◽  
Vol 12 (1) ◽  
pp. 86 ◽  
Author(s):  
Rafael Pires de Lima ◽  
Kurt Marfurt
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Network Models ◽  
Remote Sensing Image ◽  
Natural Images ◽  
Natural Image ◽  
Scene Classification ◽  
Neural Network Models

Remote-sensing image scene classification can provide significant value, ranging from forest fire monitoring to land-use and land-cover classification. Beginning with the first aerial photographs of the early 20th century to the satellite imagery of today, the amount of remote-sensing data has increased geometrically with a higher resolution. The need to analyze these modern digital data motivated research to accelerate remote-sensing image classification. Fortunately, great advances have been made by the computer vision community to classify natural images or photographs taken with an ordinary camera. Natural image datasets can range up to millions of samples and are, therefore, amenable to deep-learning techniques. Many fields of science, remote sensing included, were able to exploit the success of natural image classification by convolutional neural network models using a technique commonly called transfer learning. We provide a systematic review of transfer learning application for scene classification using different datasets and different deep-learning models. We evaluate how the specialization of convolutional neural network models affects the transfer learning process by splitting original models in different points. As expected, we find the choice of hyperparameters used to train the model has a significant influence on the final performance of the models. Curiously, we find transfer learning from models trained on larger, more generic natural images datasets outperformed transfer learning from models trained directly on smaller remotely sensed datasets. Nonetheless, results show that transfer learning provides a powerful tool for remote-sensing scene classification.

On Transfer Learning for Classifying COVID-19 in Chest X-Ray Images

2020 ◽  
Author(s):  
Elilson Santos ◽  
Lúcio Flavio De Jesus Silva ◽  
Omar Andres Carmona Cortes
Keyword(s):  
Neural Network ◽  
Infectious Disease ◽  
Decision Making ◽  
Severe Acute Respiratory Syndrome ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Decision Making Process ◽  
Network Architectures ◽  
X Ray ◽  
Chest X Ray

COVID-19 is an exceptionally infectious disease caused by severe acute respiratory syndrome. The illness has spread itself worldwide rapidly and can lead to death only in a few days. In this context, investigating fast ways of detection that help physicians in the decision-making process is essential to help in the task of saving lives. This work investigates fourteen convolutional neural network architectures using transfer learning. We used a database composed of 2,928 x-ray images divided into three classes: Normal, COVID-19, and Viral Pneumonia. Results showed that DenseNet169 presented the best results regarding classification reaching a mean accuracy of 94%, a precision of 97.6%, a recall of 95.6%, and an F1-score of 96,1%, approximately.

scholarly journals Fast COVID-19 Detection of Chest X-Ray Images Using Single Shot Detection MobileNet Convolutional Neural Networks

2021 ◽  
Vol 56 (2) ◽  
pp. 235-248
Author(s):  
Fatchul Arifin ◽  
Herjuna Artanto ◽  
Nurhasanah ◽  
Teddy Surya Gunawan
Keyword(s):  
Neural Network ◽  
Detection System ◽  
Network Models ◽  
Single Shot ◽  
Neural Network Models ◽  
X Ray ◽  
Detection Model ◽  
Shot Detection ◽  
Average Accuracy ◽  
Chest X Ray

COVID-19 is a new disease with a very rapid and tremendous spread. The most important thing needed now is a COVID-19 early detection system that is fast, easy to use, portable, and affordable. Various studies on desktop-based detection using Convolutional Neural Networks have been successfully conducted. However, no research has yet applied mobile-based detection, which requires low computational cost. Therefore, this research aims to produce a COVID-19 early detection system based on chest X-ray images using Convolutional Neural Network models to be deployed in mobile applications. It is expected that the proposed Convolutional Neural Network models can detect COVID-19 quickly, economically, and accurately. The used architecture is MobileNet's Single Shot Detection. The advantage of the Single Shot Detection MobileNet models is that they are lightweight to be applied to mobile-based devices. Therefore, these two versions will also be tested, which one is better. Both models have successfully detected COVID-19, normal, and viral pneumonia conditions with an average overall accuracy of 93.24% based on the test results. The Single Shot Detection MobileNet V1 model can detect COVID-19 with an average accuracy of 83.7%, while the Single Shot Detection MobileNet V2 Single Shot Detection model can detect COVID-19 with an average accuracy of 87.5%. Based on the research conducted, it can be concluded that the approach to detecting chest X-rays of COVID-19 can be detected using the MobileNet Single Shot Detection model. Besides, the V2 model shows better performance than the V1. Therefore, this model can be applied to increase the speed and affordability of COVID-19 detection.

scholarly journals Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-ray

2020 ◽  
Vol 10 (9) ◽  
pp. 3233 ◽  
Author(s):  
Tawsifur Rahman ◽  
Muhammad E. H. Chowdhury ◽  
Amith Khandakar ◽  
Khandaker R. Islam ◽  
Khandaker F. Islam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Viral Pneumonia ◽  
Deep Convolutional Neural Network ◽  
X Rays ◽  
X Ray ◽  
Life Threatening ◽  
Chest X Ray ◽  
The Right

Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon at the right time and thus the early diagnosis of pneumonia is vital. The paper aims to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances in accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN): AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. A total of 5247 chest X-ray images consisting of bacterial, viral, and normal chest x-rays images were preprocessed and trained for the transfer learning-based classification task. In this study, the authors have reported three schemes of classifications: normal vs. pneumonia, bacterial vs. viral pneumonia, and normal, bacterial, and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial, and viral pneumonia were 98%, 95%, and 93.3%, respectively. This is the highest accuracy, in any scheme, of the accuracies reported in the literature. Therefore, the proposed study can be useful in more quickly diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.

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