Perspective of AI system for COVID-19 detection using chest images: a review

A Novel COVID-19 Diagnostic System Using Biosensor Incorporated Artificial Intelligence Technique

COVID-19, continually developing and raising increasingly significant issues, has impacted human health and caused countless deaths. It is an infectious disease with a high incidence and mortality rate. The spread of the disease is also a significant threat to human health, especially in the developing world. This study suggests a method called shuffle shepherd optimization-based generalized deep convolutional fuzzy network (SSO-GDCFN) to diagnose the COVID-19 disease state, types, and recovered categories. The results show that the accuracy of the proposed method is as high as 99.99%; similarly, precision is 99.98%; sensitivity/recall is 100%; specificity is 95%; kappa is 0.965%; AUC is 0.88%; and MSE is less than 0.07% as well as 25 s. Moreover, the performance of the suggested method has been confirmed by comparison of the simulation results from the proposed approach with those from several traditional techniques. The experimental findings demonstrate strong performance and high accuracy for categorizing COVID-19 stages with minimal reclassifications over the conventional methods.

Quantitative texture analysis based on dynamic contrast enhanced MRI for differential diagnosis between primary thymic lymphoma from thymic carcinoma

To evaluate the value of texture analysis based on dynamic contrast enhanced MRI (DCE-MRI) in the differential diagnosis of thymic carcinoma and thymic lymphoma. Sixty-nine patients with pathologically confirmed (thymic carcinoma, n = 32; thymic lymphoma, n = 37) were enrolled in this retrospective study. Ktrans, Kep and Ve maps were automatically generated, and texture features were extracted, including mean, median, 5th/95th percentile, skewness, kurtosis, diff-variance, diff-entropy, contrast and entropy. The differences in parameters between the two groups were compared and the diagnostic efficacy was calculated. The Ktrans-related significant features yielded an area under the curve (AUC) of 0.769 (sensitivity 90.6%, specificity 51.4%) for the differentiation between thymic carcinoma and thymic lymphoma. The Kep-related significant features yielded an AUC of 0.780 (sensitivity 87.5%, specificity 62.2%). The Ve-related significant features yielded an AUC of 0.807 (sensitivity 75.0%, specificity 78.4%). The combination of DCE-MRI textural features yielded an AUC of 0.962 (sensitivity 93.8%, specificity 89.2%). Five parameters were screened out, including age, Ktrans-entropy, Kep-entropy, Ve-entropy, and Ve-P95. The combination of these five parameters yielded the best discrimination efficiency (AUC of 0.943, 93.7% sensitivity, 81.1% specificity). Texture analysis of DCE-MRI may be helpful to distinguish thymic carcinoma from thymic lymphoma.

Pneumonia Detection in Chest X-Ray Images Using Enhanced Restricted Boltzmann Machine

The process of pneumonia detection has been the focus of researchers as it has proved itself to be one of the most dangerous and life-threatening disorders. In recent years, many machine learning and deep learning algorithms have been applied in an attempt to automate this process but none of them has been successful significantly to achieve the highest possible accuracy. In a similar attempt, we propose an enhanced approach of a deep learning model called restricted Boltzmann machine (RBM) which is named enhanced RBM (ERBM). One of the major drawbacks associated with the standard format of RBM is its random weight initialization which leads to improper feature learning of the model during the training phase, resulting in poor performance of the machine. This problem has been tried to eliminate in this work by finding the differences between the means of a specific feature vector and the means of all features given as inputs to the machine. By performing this process, the reconstruction of the actual features is increased which ultimately reduces the error generated during the training phase of the model. The developed model has been applied to three different datasets of pneumonia diseases and the results have been compared with other state of the art techniques using different performance evaluation parameters. The proposed model gave highest accuracy of 98.56% followed by standard RBM, SVM, KNN, and decision tree which gave accuracies of 97.53%, 92.62%, 91.64%, and 88.77%, respectively, for dataset named dataset 2. Similarly, for the dataset 1, the highest accuracy of 96.66 has been observed for the eRBM followed by srRBM, KNN, decision tree, and SVM which gave accuracies of 90.22%, 89.34%, 87.65%, and 86.55%, respectively. In the same way, the accuracies observed for the dataset 3 by eRBM, standard RBM, KNN, decision tree, and SVM are 92.45%, 90.98%, 87.54%, 85.49%, and 84.54%, respectively. Similar observations can also be seen for other performance parameters showing the efficiency of the proposed model. As revealed in the results obtained, a significant improvement has been observed in the working of the RBM by introducing a new method of weight initialization during the training phase. The results show that the improved model outperforms other models in terms of different performance evaluation parameters, namely, accuracy, sensitivity, specificity, F1-score, and ROC curve.

Novel Coronavirus and Common Pneumonia Detection from CT Scans Using Deep Learning-Based Extracted Features

COVID-19 which was announced as a pandemic on 11 March 2020, is still infecting millions to date as the vaccines that have been developed do not prevent the disease but rather reduce the severity of the symptoms. Until a vaccine is developed that can prevent COVID-19 infection, the testing of individuals will be a continuous process. Medical personnel monitor and treat all health conditions; hence, the time-consuming process to monitor and test all individuals for COVID-19 becomes an impossible task, especially as COVID-19 shares similar symptoms with the common cold and pneumonia. Some off-the-counter tests have been developed and sold, but they are unreliable and add an additional burden because false-positive cases have to visit hospitals and perform specialized diagnostic tests to confirm the diagnosis. Therefore, the need for systems that can automatically detect and diagnose COVID-19 automatically without human intervention is still an urgent priority and will remain so because the same technology can be used for future pandemics and other health conditions. In this paper, we propose a modified machine learning (ML) process that integrates deep learning (DL) algorithms for feature extraction and well-known classifiers that can accurately detect and diagnose COVID-19 from chest CT scans. Publicly available datasets were made available by the China Consortium for Chest CT Image Investigation (CC-CCII). The highest average accuracy obtained was 99.9% using the modified ML process when 2000 features were extracted using GoogleNet and ResNet18 and using the support vector machine (SVM) classifier. The results obtained using the modified ML process were higher when compared to similar methods reported in the extant literature using the same datasets or different datasets of similar size; thus, this study is considered of added value to the current body of knowledge. Further research in this field is required to develop methods that can be applied in hospitals and can better equip mankind to be prepared for any future pandemics.