Automatically identify COVID-19 and Pneumonia patients using Deep learning in Chest X-rays image

The coronavirus pandemic has led to an immense loss of human life worldwide. It poses an unprecedented challenge to the education system, economic system, food systems, public health, and the job market. Approximately 220 nations, regions, or territories are now affected by coronavirus disease, and the World Health Organization has recorded about 1,535,982 deaths. The reverse transcription polymerase chain reaction (RT-PCR) or rapid antigen test or computed tomography (CT)-based detection techniques lack rapid testing property due to their low sensitivity and availability issues. Therefore, the use of deep convolutional neural networks (DCNN) to learn and analyze the chest X-ray signatures of COVID-19 patients is becoming crucial. This chapter gives a brief introduction two various deep learning architectures used for chest X-ray analysis for COVID-19 detection, along with their pros and cons. Further in this chapter, it is proposed that there should be a two-stage DCNN architecture to learn the imaging hallmarks of normal, pneumonia, and COVID-19 diseases from chest X-ray images and predict those lung diseases without human intervention. The main objective of this study is to precisely extract the region of interest, i.e., the lung contours, from the chest X-ray images and feed them to the classification network. It directly helps to increase the diagnosis sensitivity, reduce diagnosis time, and enable easier analysis and classification of chest-related diseases. It also helps to efficiently train the deep learning network, even with a minimal number of training samples. The experimental results demonstrate that the proposed model’s misclassification rate is low. The proposed model outperforms other state-of-the-art studies and achieves a COVID-19 diagnosis sensitivity of 99% and an overall accuracy of 98%. The proposed model can be used as a reliable rapid testing tool for COVID-19 disease but not as an alternative to RT-PCR. The performance of the model was also tested in Indian COVID-19 patients. Ultimately, this low-cost, intelligent two-stage DCNN provides a detailed understanding that can be useful to improve patient diagnostic decisions made by medical professionals.

Coronavirus disease-19 (COVID-19) has rapidly spread all over the world. It is found that the low sensitivity of reverse transcription-polymerase chain reaction (RT-PCR) examinations during the early stage of COVID-19 disease. Thus, efficient models are desirable for early-stage testing of COVID-19 infected patients. Chest X-ray (CXR) images of COVID-19 infected patients have shown some bilateral changes. In this paper, deep transfer learning and a deep forest-based model are proposed to diagnose COVID-19 infection from CXR images. Initially, features of X-ray images are extracted using the well-known deep transfer learning model (i.e., ResNet101), which does not require tuning many parameters compared to the deep convolutional neural network (CNN). After that, the deep forest model is utilised to predict COVID-19 infected patients. The deep forest is based upon ensemble learning and requires a small number of hyper-parameters. Additionally, the proposed model is trained on a multi-class dataset that contains four different classes as COVID-19 (+), pneumonia, tuberculosis, and healthy patients. The comparisons are drawn among the proposed deep transfer learning and deep forest-based models, the competitive models. The obtained results show that the proposed model effectively diagnoses COVID-19 infection with an accuracy of 99.4%.

PurposeTuberculosis (TB) is a severe bacterial infectious lung disease. Millions of people die or experience severe health complications due to TB each year. Accurate, automated, and effective detection of TB is key to curing and preventing person-to-person transmission. In this regard, deep learning (DL) and machine learning (ML) techniques applied to chest X-ray (CXR) images have proved effective. Here, we present our DL- and ML-based approach for TB detection using CXR images.Material and methodsWe implemented convolutional neural network (CNN)-based pre-trained DL models, such as DenseNet121, DenseNet169, DenseNet201, ResNet152, and VGG19, as feature extractors. ML models, including support vector machine (SVM), XGBoost, logistic regression, and a DL-based custom model, were used as classifiers. A total of 2,391 CXR images from three publicly available datasets were considered.ResultsWe found three models achieving the highest value in different evaluation metrics: accuracy (99.91%) with ResNet152 and SVM; recall (99.22%), precision (99.23%), and F1-score (99.22%) with DenseNet169 and the custom classifier; and area under the curve (99.99%) with DenseNet201 and the custom classifier. Among these models, we propose DenseNet169 with the custom classifier as the best performer for potential clinical application, as it reflected a high and well-balanced performance across all evaluation metrics.ConclusionsThis study evaluated pre-trained CNN-based DL and ML models on pre-processed CXR images for the detection of TB. The DenseNet169 model with a custom classifier stands out with its high and well-balanced performance, offering a significant contribution to the effective and automated detection of TB.

An official announcement by the United Nations agency has confirmed an enduring deadly disease, namely coronavirus (COVID-19). While comparing to traditional methods such as blood tests and analysis, chest X-ray (CXR) images are the most important evidential techniques to find whether the patient is normal or COVID-19. In order to distinguish the COVID-19 patient among these images there is a classification method via convolution neural network (NN) techniques adapted. In this chapter, the experimental outcomes in which histogram equalization approach is utilized to process the input CXR images. To find accurate results deep learning (DL)-based convolution neural network (CNN) achieves accuracy as 50%, subsequently reshaping the input images into 224×224 along with batch size 32 undergoes training phase and it consists of 10 epochs to generate enhanced accuracy as 96.4% by tuning hyperparameters. Moreover, we applied optimization algorithm such as Adam optimizer, RMSprop optimizer and stochastic gradient descent (SGD) in which Adam and SGD achieves validation accuracy as 96.67% yields better performance in classifying images as normal and Coronavirus disease patients.

COVID-19 has engulfed over 200 nations through human-to-human transmission, either directly or indirectly. Reverse Transcription-polymerase Chain Reaction (RT-PCR) has been endorsed as a standard COVID-19 diagnostic procedure but has caveats such as low sensitivity, the need for a skilled workforce, and is time-consuming. Coronaviruses show significant manifestation in Chest X-Ray (CX-Ray) images and, thus, can be a viable option for an alternate COVID-19 diagnostic strategy. An automatic COVID-19 detection system can be developed to detect the disease, thus reducing strain on the healthcare system. This paper discusses a real-time Convolutional Neural Network (CNN)based system for COVID-19 illness prediction from CX-Ray images on the cloud. The implemented CNN model displays exemplary results, with training accuracy being 99.94% and validation accuracy reaching 98.81%. The confusion matrix was utilized to assess the models' outcome and achieved 99% precision, 98% recall, 99% F1 score, 100% training area under the curve (AUC) and 98.3% validation AUC. The same CX-Ray dataset was also employed to predict the COVID-19 disease with deep Convolution Neural Networks (DCNN), such as ResNet50, VGG19, InceptonV3, and Xception. The prediction outcome demonstrated that the present CNN was more capable than the DCNN models. The efficient CNN model was deployed to the Platform as a Service (PaaS) cloud.

Bi-Level Prediction Model for Screening COVID-19 Patients Using Chest X-Ray Images

Novel coronavirus disease (COVID-19) is an extremely contagious and quickly spreading coronavirus infestation. Severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), which outbreak in 2002 and 2011, and the current COVID-19 pandemic are all from the same family of coronavirus. This work aims to classify COVID-19, SARS, and MERS chest X-ray (CXR) images using deep convolutional neural networks (CNNs). To the best of our knowledge, this classification scheme has never been investigated in the literature. A unique database was created, so-called QU-COVID-family, consisting of 423 COVID-19, 144 MERS, and 134 SARS CXR images. Besides, a robust COVID-19 recognition system was proposed to identify lung regions using a CNN segmentation model (U-Net), and then classify the segmented lung images as COVID-19, MERS, or SARS using a pre-trained CNN classifier. Furthermore, the Score-CAM visualization method was utilized to visualize classification output and understand the reasoning behind the decision of deep CNNs. Several deep learning classifiers were trained and tested; four outperforming algorithms were reported: SqueezeNet, ResNet18, InceptionV3, and DenseNet201. Original and preprocessed images were used individually and all together as the input(s) to the networks. Two recognition schemes were considered: plain CXR classification and segmented CXR classification. For plain CXRs, it was observed that InceptionV3 outperforms other networks with a 3-channel scheme and achieves sensitivities of 99.5%, 93.1%, and 97% for classifying COVID-19, MERS, and SARS images, respectively. In contrast, for segmented CXRs, InceptionV3 outperformed using the original CXR dataset and achieved sensitivities of 96.94%, 79.68%, and 90.26% for classifying COVID-19, MERS, and SARS images, respectively. The classification performance degrades with segmented CXRs compared to plain CXRs. However, the results are more reliable as the network learns from the main region of interest, avoiding irrelevant non-lung areas (heart, bones, or text), which was confirmed by the Score-CAM visualization. All networks showed high COVID-19 detection sensitivity (> 96%) with the segmented lung images. This indicates the unique radiographic signature of COVID-19 cases in the eyes of AI, which is often a challenging task for medical doctors.

Introduction: The early automatic diagnosis of the novel coronavirus (COVID-19) disease could be very helpful to reduce its spread around the world. In this study, we revisit the identification of COVID-19 from chest X-ray images using deep learning. Methods: We collected a relatively large COVID-19 dataset—compared with previous studies—containing 309 real COVID-19 chest X-ray images. We also prepared 2,000 chest X-ray images of pneumonia cases and 1,000 images of healthy controls. Deep transfer learning was used to detect abnormalities in our image dataset. We fine-tuned three, pre-trained convolutional neural network (CNN) models on a training dataset: DenseNet 121, NASNetLarge, and NASNet-Mobile. Results: The evaluation of our models on a test dataset showed that these models achieved an average sensitivity rate of approximately 99.45% and an average specificity rate of approximately 99.5%. Conclusion: A larger dataset of COVID-19 X-ray images could lead to more accurate and reliable identification of COVID-19 infections using deep transfer learning. However, the clinical diagnosis of COVID-19 disease is always necessary.

The research community has recently shown significant interest in designing automated systems to detect coronavirus disease 2019 (COVID-19) using deep learning approaches and chest radiography images. However, state-of-the-art deep learning techniques, especially convolutional neural networks (CNNs), demand more learnable parameters and memory. Therefore, they may not be suitable for real-time diagnosis. Thus, the design of a lightweight CNN model for fast and accurate COVID-19 detection is an urgent need. In this paper, a lightweight CNN model called LW-CORONet is proposed that comprises a sequence of convolution, rectified linear unit (ReLU), and pooling layers followed by two fully connected layers. The proposed model facilitates extracting meaningful features from the chest X-ray (CXR) images with only five learnable layers. The proposed model is evaluated using two larger CXR datasets (Dataset-1: 2250 images and Dataset-2: 15,999 images) and the classification accuracy obtained are 98.67% and 99.00% on Dataset-1 and 95.67% and 96.25% on Dataset-2 for multi-class and binary classification cases, respectively. The results are compared with four contemporary pre-trained CNN models as well as state-of-the-art models. The effect of several hyperparameters: different optimization techniques, batch size, and learning rate have also been investigated. The proposed model demands fewer parameters and requires less memory space. Hence, it is effective for COVID-19 detection and can be utilized as a supplementary tool to assist radiologists in their diagnosis.

The coronavirus disease 2019 (COVID-19) led to a dramatic increase in the number of cases of patients with pneumonia worldwide. In this study, we aimed to develop an AI-assisted multistrategy image enhancement technique for chest X-ray (CXR) images to improve the accuracy of COVID-19 classification. Our new classification strategy consisted of 3 parts. First, the improved U-Net model with a variational encoder segmented the lung region in the CXR images processed by histogram equalization. Second, the residual net (ResNet) model with multidilated-rate convolution layers was used to suppress the bone signals in the 217 lung-only CXR images. A total of 80% of the available data were allocated for training and validation. The other 20% of the remaining data were used for testing. The enhanced CXR images containing only soft tissue information were obtained. Third, the neural network model with a residual cascade was used for the super-resolution reconstruction of low-resolution bone-suppressed CXR images. The training and testing data consisted of 1,200 and 100 CXR images, respectively. To evaluate the new strategy, improved visual geometry group (VGG)-16 and ResNet-18 models were used for the COVID-19 classification task of 2,767 CXR images. The accuracy of the multistrategy enhanced CXR images was verified through comparative experiments with various enhancement images. In terms of quantitative verification, 8-fold cross-validation was performed on the bone suppression model. In terms of evaluating the COVID-19 classification, the CXR images obtained by the improved method were used to train 2 classification models. Compared with other methods, the CXR images obtained based on the proposed model had better performance in the metrics of peak signal-to-noise ratio and root mean square error. The super-resolution CXR images of bone suppression obtained based on the neural network model were also anatomically close to the real CXR images. Compared with the initial CXR images, the classification accuracy rates of the internal and external testing data on the VGG-16 model increased by 5.09% and 12.81%, respectively, while the values increased by 3.51% and 18.20%, respectively, for the ResNet-18 model. The numerical results were better than those of the single-enhancement, double-enhancement, and no-enhancement CXR images. The multistrategy enhanced CXR images can help to classify COVID-19 more accurately than the other existing methods.

The unexpected outbreak of the corona virus in 2019 has had a devastating impact around the world, resulting in unprecedented circumstances. The corona virus spread at an exponential rate, creating a severe threat to human health as well as the world economy. This situation necessitates rapid detection of the virus and necessary healthcare facilities, as the virus can cause severe casualties if left untreated. Machine learning has advanced to the point that it can now solve a wide range of biomedical problems with high precision. The research focuses on using a deep learning mechanism to identify chest X-ray images of Covid-19 and other pneumonia patients in two- and three-class scenarios. The proposed approach employs the GoogLeNet architecture to extract features that are fed into different classifiers. With the Bayesian Optimisation technique, Kernel Support Vector Machineis found to be the most accurate among various classifiers compared. The model showed an overall accuracy of 98.31% for two-class classification between Covid-19 and non-Covid chest X-ray images and 98.60% overall accuracy for three-class classification problem between Covid-19, healthy and viral pneumonia X-ray images. The proposed system outperformed several existing architectures, and it was also tested using smaller datasets so as to ensure robustness.

COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images

Deep learning approaches for COVID-19 detection based on chest X-ray images

Pneumonia, a condition caused by infectious agents such as bacteria, viruses, fungi, or parasites, results in lung infections and the accumulation of pus in affected tissues. Incorrect diagnosis or improper treatment can significantly impact a patient's quality of life. This project focuses on the precise identification and categorization of pneumonia-afflicted patients through the analysis of their chest X-rays. Leveraging advancements in deep learning, healthcare experts can make more accurate diagnostic decisions for various illnesses. This study presents an innovative approach utilizing Convolutional Neural Networks (CNN) to predict and distinguish between patients affected by pneumonia and those who are not, based on chest X-ray images. The methodology involves a series of convolutional and max pooling layers activated using the ReLU activation function. Subsequently, the processed data is passed through dense layers, culminating in the activation of the output neuron through the sigmoidal function. During the training process, the model exhibits increasing accuracy and decreasing loss, demonstrating its effectiveness. To prevent overfitting, data augmentation techniques are applied before model fitting. These strategies collectively enhance the robustness and reliability of the deep learning models, resulting in accurate and persuasive detection of pneumonia from chest X-ray images. Keywords: Pneumonia, CNN, VGG-16, VGG-19, Chest X-rays, Classification, Deeep learning, Data augmenatation, ReLU, softmax.

The occurrence of COVID-19 disease and the pandemic has caused severe detrimental effects on global health, economy, and well-being. Ignorance and disobeying precautionary measures result in the rapid spread of this contagious infection and subsequent illnesses. There have been more than forty crore cases and fifty lakh deaths reported worldwide. Though the RT-PCR manual testing is widely carried out for diagnosing the presence of the virus in the human body, it is less reliable than chest CT and X-Ray imaging techniques. These tests are swift and assist in determining the severity of the infection with affordable costs. Hence, recent advances involve the application of artificial intelligence and deep learning techniques for automatic detection of this disease. Various datasets of X-Ray images of the chest are available online which are utilized for training and validation of results. In this paper, a dense layer Convolutional Neural Network with one twenty one layers model is used to predict the pneumonia type from the Chest X-Ray images. The results acquired from the implementation show that the model has the highest accuracy of 0.9738 while the specificity is 93.6%, and it outperforms the other similar models. Hence, the possibility of a false positive case occurring is lower and can facilitate easy diagnosis of the infection.