Automated Diagnosis of COVID-19 Using Deep Features and Parameter Free BAT Optimization

Deep Learning Methods for Interpretation of Pulmonary CT and X-ray Images in Patients with COVID-19-Related Lung Involvement: A Systematic Review

SARS-CoV-2 is a novel virus that has been affecting the global population by spreading rapidly and causing severe complications, which require prompt and elaborate emergency treatment. Automatic tools to diagnose COVID-19 could potentially be an important and useful aid. Radiologists and clinicians could potentially rely on interpretable AI technologies to address the diagnosis and monitoring of COVID-19 patients. This paper aims to provide a comprehensive analysis of the state-of-the-art deep learning techniques for COVID-19 classification. The previous studies are methodically evaluated, and a summary of the proposed convolutional neural network (CNN)-based classification approaches is presented. The reviewed papers have presented a variety of CNN models and architectures that were developed to provide an accurate and quick automatic tool to diagnose the COVID-19 virus based on presented CT scan or X-ray images. In this systematic review, we focused on the critical components of the deep learning approach, such as network architecture, model complexity, parameter optimization, explainability, and dataset/code availability. The literature search yielded a large number of studies over the past period of the virus spread, and we summarized their past efforts. State-of-the-art CNN architectures, with their strengths and weaknesses, are discussed with respect to diverse technical and clinical evaluation metrics to safely implement current AI studies in medical practice.

Fast COVID-19 Detection from Chest X-Ray Images Using DCT Compression

Novel coronavirus (COVID-19) is a new strain of coronavirus, first identified in a cluster with pneumonia symptoms caused by SARS-CoV-2 virus. It is fast spreading all over the world. Most infected people will develop mild to moderate illness and recover without hospitalization. Currently, real-time quantitative reverse transcription-PCR (rqRT-PCR) is popular for coronavirus detection due to its high specificity, simple quantitative analysis, and higher sensitivity than conventional RT-PCR. Antigen tests are also commonly used. It is very essential for the automatic detection of COVID-19 from publicly available resources. Chest X-ray (CXR) images are used for the classification of COVID-19, normal, and viral pneumonia cases. The CXR images are divided into sub-blocks for finding out the discrete cosine transform (DCT) for every sub-block in this proposed method. In order to produce a compressed version for each CXR image, the DCT energy compaction capability is used. For each image, hardly few spectral DCT components are included as features. The dimension of the final feature vectors is reduced by scanning the compressed images using average pooling windows. In the 3-set classification, a multilayer artificial neural network is used. It is essential to triage non-COVID-19 patients with pneumonia to give out hospital resources efficiently. Higher size feature vectors are used for designing binary classification for COVID-19 and pneumonia. The proposed method achieved an average accuracy of 95% and 94% for the 3-set classification and binary classification, respectively. The proposed method achieves better accuracy than that of the recent state-of-the-art techniques. Also, the time required for the implementation is less.

Vulture-Based AdaBoost-Feedforward Neural Frame Work for COVID-19 Prediction and Severity Analysis System

In today's scenario, many scientists and medical researchers have been involved in deep research for discovering the desired medicine to reduce the spread of COVID-19 disease. However, still, it is not the end. Hence, predicting the COVID possibility in an early stage is the most required matter to reduce the death risks. Therefore, many researchers have focused on designing an early prediction mechanism in the basis of deep learning (DL), machine learning (Ml), etc., on detecting the COVID virus and severity in the human body in an earlier stage. However, the complexity of X-ray images has made it difficult to attain the finest prediction accuracy. Hence, the present research work has aimed to develop a novel Vulture Based Adaboost-Feedforward Neural (VbAFN) scheme to forecast the COVID-19 severity early. Here, the chest X-ray images were employed to identify the COVID risk feature in humans. The preprocessing function is done in the initial phase; the error-free data is imported to the classification layer for the feature extraction and segmentation process. This investigation aims to track and segment the affected parts from the trained X-ray images by the vulture fitness and to segment them with a good exactness rate. Subsequently, the designed model has gained a better segmentation accuracy of 99.9% and a lower error rate of 0.0145, which is better than other compared models. Hence, this proposed model in medical applications will offer the finest results.

Machine Learning with Quantum Seagull Optimization Model for COVID-19 Chest X-Ray Image Classification

Early and accurate detection of COVID-19 is an essential process to curb the spread of this deadly disease and its mortality rate. Chest radiology scan is a significant tool for early management and diagnosis of COVID-19 since the virus targets the respiratory system. Chest X-ray (CXR) images are highly useful in the effective detection of COVID-19, thanks to its availability, cost-effective means, and rapid outcomes. In addition, Artificial Intelligence (AI) techniques such as deep learning (DL) models play a significant role in designing automated diagnostic processes using CXR images. With this motivation, the current study presents a new Quantum Seagull Optimization Algorithm with DL-based COVID-19 diagnosis model, named QSGOA-DL technique. The proposed QSGOA-DL technique intends to detect and classify COVID-19 with the help of CXR images. In this regard, the QSGOA-DL technique involves the design of EfficientNet-B4 as a feature extractor, whereas hyperparameter optimization is carried out with the help of QSGOA technique. Moreover, the classification process is performed by a multilayer extreme learning machine (MELM) model. The novelty of the study lies in the designing of QSGOA for hyperparameter optimization of the EfficientNet-B4 model. An extensive series of simulations was carried out on the benchmark test CXR dataset, and the results were assessed under different aspects. The simulation results demonstrate the promising performance of the proposed QSGOA-DL technique compared to recent approaches.

Classifier Fusion for Detection of COVID-19 from CT Scans

The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. COVID-19 is found to be the most infectious disease in last few decades. This disease has infected millions of people worldwide. The inadequate availability and the limited sensitivity of the testing kits have motivated the clinicians and the scientist to use Computer Tomography (CT) scans to screen COVID-19. Recent advances in technology and the availability of deep learning approaches have proved to be very promising in detecting COVID-19 with increased accuracy. However, deep learning approaches require a huge labeled training dataset, and the current availability of benchmark COVID-19 data is still small. For the limited training data scenario, the CNN usually overfits after several iterations. Hence, in this work, we have investigated different pre-trained network architectures with transfer learning for COVID-19 detection that can work even on a small medical imaging dataset. Various variants of the pre-trained ResNet model, namely ResNet18, ResNet50, and ResNet101, are investigated in the current paper for the detection of COVID-19. The experimental results reveal that transfer learned ResNet50 model outperformed other models by achieving a recall of 98.80% and an F1-score of 98.41%. To further improvise the results, the activations from different layers of best performing model are also explored for the detection using the support vector machine, logistic regression and K-nearest neighbor classifiers. Moreover, a classifier fusion strategy is also proposed that fuses the predictions from the different classifiers via majority voting. Experimental results reveal that via using learned image features and classification fusion strategy, the recall, and F1-score have improvised to 99.20% and 99.40%.

xViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography

Objective: Since its outbreak, the rapid spread of COrona VIrus Disease 2019 (COVID-19) across the globe has pushed the health care system in many countries to the verge of collapse. Therefore, it is imperative to correctly identify COVID-19 positive patients and isolate them as soon as possible to contain the spread of the disease and reduce the ongoing burden on the healthcare system. The primary COVID-19 screening test, RT-PCR although accurate and reliable, has a long turn-around time. In the recent past, several researchers have demonstrated the use of Deep Learning (DL) methods on chest radiography (such as X-ray and CT) for COVID-19 detection. However, existing CNN based DL methods fail to capture the global context due to their inherent image-specific inductive bias. Methods: Motivated by this, in this work, we propose the use of vision transformers (instead of convolutional networks) for COVID-19 screening using the X-ray and CT images. We employ a multi-stage transfer learning technique to address the issue of data scarcity. Furthermore, we show that the features learned by our transformer networks are explainable. Results: We demonstrate that our method not only quantitatively outperforms the recent benchmarks but also focuses on meaningful regions in the images for detection (as confirmed by Radiologists), aiding not only in accurate diagnosis of COVID-19 but also in localization of the infected area. The code for our implementation can be found here - https://github.com/arnabkmondal/xViTCOS. Conclusion: The proposed method will help in timely identification of COVID-19 and efficient utilization of limited resources.

Adaptive UNet-based Lung Segmentation and Ensemble Learning with CNN-based Deep Features for Automated COVID-19 Diagnosis

COVID-19 disease is a major health calamity in twentieth century, in which the infection is spreading at the global level. Developing countries like Bangladesh, India, and others are still facing a delay in recognizing COVID-19 cases. Hence, there is a need for immediate recognition with perfect identification of infection. This clear visualization helps to save the life of suspected COVID-19 patients. With the help of traditional RT-PCR testing, the combination of medical images and deep learning classifiers delivers more hopeful results with high accuracy in the prediction and recognition of COVID-19 cases. COVID-19 disease is recently researched through sample chest X-ray images, which have already proven its efficiency in lung diseases. To emphasize corona virus testing methods and to control the community spreading, the automatic detection process of COVID-19 is processed through the detailed medication reports from medical images. Although there are numerous challenges in the manual understanding of traces in COVID-19 infection from X-ray, the subtle differences among normal and infected X-rays can be traced by the data patterns of Convolutional Neural Network (CNN). To improve the detection performance of CNN, this paper plans to develop an Ensemble Learning with CNN-based Deep Features (EL-CNN-DF). In the initial phase, image scaling and median filtering perform the pre-processing of the chest X-ray images gathered from the benchmark source. The second phase is lung segmentation, which is the significant step for COVID detection. It is accomplished by the Adaptive Activation Function-based U-Net (AAF-U-Net). Once the lungs are segmented, it is subjected to novel EL-CNN-DF, in which the deep features are extracted from the pooling layer of CNN, and the fully connected layer of CNN are replaced with the three classifiers termed "Support Vector Machine (SVM), Autoencoder, Naive Bayes (NB)". The final detection of COVID-19 is done by these classifiers, in which high ranking strategy is utilized. As a modification, a Self Adaptive-Tunicate Swarm Algorithm (SA-TSA) is adopted as a boosting algorithm to enhance the performance of segmentation and detection. The overall analysis has shown that the precision of the enhanced CNN by using SA-TSA was 1.02%, 4.63%, 3.38%, 1.62%, 1.51% and 1.04% better than SVM, autoencoder, NB, Ensemble, RNN and LSTM respectively. The comparative performance analysis on existing model proves that the proposed algorithm is better than other algorithms in terms of segmentation and classification of COVID-19 detection.

Deep Learning-Based Approaches to Improve Classification Parameters for Diagnosing COVID-19 from CT Images

Patients infected with the COVID-19 virus develop severe pneumonia, which generally leads to death. Radiological evidence has demonstrated that the disease causes interstitial involvement in the lungs and lung opacities, as well as bilateral ground-glass opacities and patchy opacities. In this study, new pipeline suggestions are presented, and their performance is tested to decrease the number of false-negative (FN), false-positive (FP), and total misclassified images (FN + FP) in the diagnosis of COVID-19 (COVID-19/non-COVID-19 and COVID-19 pneumonia/other pneumonia) from CT lung images. A total of 4320 CT lung images, of which 2554 were related to COVID-19 and 1766 to non-COVID-19, were used for the test procedures in COVID-19 and non-COVID-19 classifications. Similarly, a total of 3801 CT lung images, of which 2554 were related to COVID-19 pneumonia and 1247 to other pneumonia, were used for the test procedures in COVID-19 pneumonia and other pneumonia classifications. A 24-layer convolutional neural network (CNN) architecture was used for the classification processes. Within the scope of this study, the results of two experiments were obtained by using CT lung images with and without local binary pattern (LBP) application, and sub-band images were obtained by applying dual-tree complex wavelet transform (DT-CWT) to these images. Next, new classification results were calculated from these two results by using the five pipeline approaches presented in this study. For COVID-19 and non-COVID-19 classification, the highest sensitivity, specificity, accuracy, F-1, and AUC values obtained without using pipeline approaches were 0.9676, 0.9181, 0.9456, 0.9545, and 0.9890, respectively; using pipeline approaches, the values were 0.9832, 0.9622, 0.9577, 0.9642, and 0.9923, respectively. For COVID-19 pneumonia/other pneumonia classification, the highest sensitivity, specificity, accuracy, F-1, and AUC values obtained without using pipeline approaches were 0.9615, 0.7270, 0.8846, 0.9180, and 0.9370, respectively; using pipeline approaches, the values were 0.9915, 0.8140, 0.9071, 0.9327, and 0.9615, respectively. The results of this study show that classification success can be increased by reducing the time to obtain per-image results through using the proposed pipeline approaches.