A Novel Explainable CNN Model for Screening COVID-19 on X-ray Images

Due to the rapid propagation characteristic of the Coronavirus (COV-ID-19) disease, manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of infection. Despite, new automated diagnostic methods have been brought on board, particularly methods based on artificial intelligence using different medical data such as X-ray imaging. Thoracic imaging, for example, produces several image types that can be processed and analyzed by machine and deep learning methods. X-ray imaging materials widely exist in most hospitals and health institutes since they are affordable compared to other imaging machines. Through this paper, we propose a novel Convolutional Neural Network (CNN) model (COV2Net) that can detect COVID-19 virus by analyzing the X-ray images of suspected patients. This model is trained on a dataset containing thousands of X-ray images collected from different sources. The model was tested and evaluated on an independent dataset. In order to approve the performance of the proposed model, three CNN models namely MobileNet, Residential Energy Services Network (Res-Net), and Visual Geometry Group 16 (VGG-16) have been implemented using transfer learning technique. This experiment consists of a multi-label classification task based on X-ray images for normal patients, patients infected by COVID-19 virus and other patients infected with pneumonia. This proposed model is empowered with Gradient-weighted Class Activation Mapping (Grad-CAM) and Grad-Cam++ techniques for a visual explanation and methodology debugging goal. The finding results show that the proposed model COV2Net outperforms the state-of-the-art methods. © 2023 CRL Publishing. All rights reserved.

Combining cnn and grad-cam for covid-19 disease prediction and visual explanation

With daily increasing of suspected COVID-19 cases, the likelihood of the virus mutation increases also causing the appearance of virulent variants hav-ing a high level of replication. Automatic diagnosis methods of COVID-19 disease are very important in the medical community. An automatic diagnosis could be performed using machine and deep learning techniques to analyze and classify different lung X-ray images. Many research studies proposed automatic methods for detecting and predicting COVID-19 patients based on their clinical data. In the leak of valid X-ray images for patients with COVID-19 datasets, several researchers proposed to use augmentation techniques to bypass this limitation. However, the obtained results by augmentation techniques are not efficient to be projected for the real world. In this paper, we propose a convolutional neural network (CNN)-based method to analyze and distinguish COVID-19 cases from other pneumonia and normal cases using the transfer learning technique. To help doctors easily interpret the results, a recent visual explanation method called Gradient-weighted Class Activation Mapping (Grad-CAM) is applied for each class. This technique is used in order to highlight the regions of interest on the X-ray image, so that, the model prediction result can be easily interpreted by the doctors. This method allows doctors to focus only on the important parts of the image and evaluate the efficiency of the concerned model. Three selected deep learning models namely VGG16, VGG19, and MobileNet, were used in the experiments with transfer learning technique. To bypass the limitation of the leak of lung X-ray images of patients with COVID-19 disease, we propose to combine several different datasets in order to assemble a new dataset with sufficient real data to accomplish accurately the training step. The best results were obtained using the tuned VGG19 model with 96.97% accuracy, 100% precision, 100% F1-score, and 99% recall. © 2022, Tech Science Press. All rights reserved.

Diagnosis of COVID-19 Disease Using Convolutional Neural Network Models Based Transfer Learning

COVID-19 disease is similar to normal pneumonia caused by bacteria or other viruses. Therefore, the manual classification of lung diseases is very hard to discover, particularly the distinction between COVID-19 and NON-COVID-19 disease. COVID-19 causes infections on one or both lungs which appear as inflammations across lung cells. This can lead to dangerous complications that might cause death in the case of gaining or having an immune disease. The problem of COVID-19 is that its symptoms are similar to conventional chest respiratory diseases like flu disease and chest pain while breathing or coughing produces mucus, high fever, absence of appetite, abdominal pain, vomiting, and diarrhea. In most cases, a deep manual analysis of the chest’s X-ray or computed tomography (CT) image can lead to an authentic diagnosis of COVID-19. Otherwise, manual analysis is not sufficient to distinguish between pneumonia and COVID-19 disease. Thus, specialists need additional expensive tools to confirm their initial hypothesis or diagnosis using real-time polymerase chain reaction (RT-PCR) test or MRI imaging. However, a traditional diagnosis of COVID-19 or other pneumonia takes a lot of time from specialists, which is so significant parameter in the case of a pandemic, whereas, a lot of patients are surcharging hospital services. In such a case, an automatic method for analyzing x-ray chest images is needed. In this regard, the research work has taken advantage of proposing a convolutional neural network method for COVID-19 and pneumonia classification. The X-ray processing have been chosen as a diagnosis way because of its availability in hospitals as a cheap imaging tool compared to other technologies. In this work, three CNN models based on VGG-16, VGG19, and MobileNet were trained using the zero-shot transfer learning technique. The best results are obtained on VGG-19 based model: 96.97% accuracy, 100% precision, 100% F1-score, and 99% recall. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Performance Evaluation of Transfer Learning Technique for Automatic Detection of Patients with COVID-19 on X-Ray Images

A new pandemic of coronavirus (COVID19) reported for the first time in Wuhan, China. This new virus has spread rapidly around the world with fever, cough, and difficulty breathing symptoms. In this paper, we propose a Deep Learning based system for the diagnosis of COVID19 disease. This system is based on Transfer Learning technique of six pretrained models. The X-Ray image dataset used contains 2905 images with a resolution of 1024*1024 pixels. A series of preprocessing operations has been applied to this dataset. The performance results obtained in this study confirm that the classification obtained by the Xception network is the most precise for detecting cases infected with COVID19. Our system has achieved accuracy and sensitivity of 98% and 100% respectively. © 2020 IEEE.

Convolutional Neural Network Based Classification of Patients with Pneumonia using X-ray Lung Images

Analysis and classification of lung diseases using X-ray images is a primary step in the procedure of pneumonia diagnosis, especially in a critical period as pandemic of COVID-19 that is type of pneumonia. Therefore, an automatic method with high accuracy of classification is needed to perform classification of lung diseases due to the increasing number of cases. Convolutional Neural Networks (CNN) based classification has gained a big popularity over the last few years because of its speed and level of accuracy on the image’s classification tasks. Through this article, we propose an implementation a CNN-based classification models using transfer learning technique to perform pneumonia detection and compare the results in order to detect the best model for the task according to certain parameters. As this has become a fast expanding field, there are several models but we will focus on the best outperforming algorithms according to their architecture, length and type of layers and evaluation parameters for the classification tasks. Firstly, we review the existing conventional methods and deep learning architectures used for segmentation in general. Next, we perform a deep performance and analysis based on accuracy and loss function of implemented models. A critical analysis of the results is made to highlight all important issues to improve. © 2020 ASTES Publishers. All rights reserved.