Detection of COVID-19 using CXR and CT images using Transfer Learning and Haralick features

Recognition of COVID-19 is a challenging task which consistently requires taking a gander at clinical images of patients. In this paper, the transfer learning technique has been applied to clinical images of different types of pulmonary diseases, including COVID-19. It is found that COVID-19 is very much similar to pneumonia lung disease. Further findings are made to identify the type of pneumonia similar to COVID-19. Transfer Learning makes it possible for us to find out that viral pneumonia is same as COVID-19. This shows the knowledge gained by model trained for detecting viral pneumonia can be transferred for identifying COVID-19. Transfer Learning shows significant difference in results when compared with the outcome from conventional classifications. It is obvious that we need not create separate model for classifying COVID-19 as done by conventional classifications. This makes the herculean work easier by using existing model for determining COVID-19. Second, it is difficult to detect the abnormal features from images due to the noise impedance from lesions and tissues. For this reason, texture feature extraction is accomplished using Haralick features which focus only on the area of interest to detect COVID-19 using statistical analyses. Hence, there is a need to propose a model to predict the COVID-19 cases at the earliest possible to control the spread of disease. We propose a transfer learning model to quicken the prediction process and assist the medical professionals. The proposed model outperforms the other existing models. This makes the time-consuming process easier and faster for radiologists and this reduces the spread of virus and save lives.

Detection of COVID-19 from Chest CT Images Using CNN with MLP Hybrid Model.

COVID-19 when left undetected can lead to a hazardous infection spread, leading to an unfortunate loss of life. It's of utmost importance to diagnose COVID-19 in Infected patients at the earliest, to avoid further complications. RT-PCR, the gold standard method is routinely used for the diagnosis of COVID-19 infection. Yet, this method comes along with few limitations such as its time-consuming nature, a scarcity of trained manpower, sophisticated laboratory equipment and the possibility of false positive and negative results. Physicians and global health care centers use CT scan as an alternate for the diagnosis of COVID-19. But this process of detection too, might demand more manual work, effort and time. Thus, automating the detection of COVID-19 using an intelligent system has been a recent research topic, in the view of pandemic. This will also help in saving the physician's time for carrying out further treatment. In this paper, a hybrid learning model has been proposed to identify the COVID-19 infection using CT scan images. The Convolutional Neural Network (CNN) was used for feature extraction and Multilayer Perceptron was used for classification. This hybrid learning model's results were also compared with traditional CNN and MLP models in terms of Accuracy, F1-Score, Precision and Recall. This Hybrid CNN-MLP model showed an Accuracy of 94.89% when compared with CNN and MLP giving 86.95% and 80.77% respectively.

Prediction of COVID Criticality Score with Laboratory, Clinical and CT Images using Hybrid Regression Models.

BACKGROUND AND OBJECTIVE: Rapid and precise diagnosis of COVID-19 is very critical in hotspot regions. The main aim of this proposed work is to investigate the baseline, laboratory and CT features of COVID-19 affected patients of two groups (Early and Critical stages). The detection model for COVID-19 is built depending upon the manifestations that define the severity of the disease. METHODS: The CT scan images are fed into the various deep learning, machine learning and hybrid learning models to mine the necessary features and predict CT Score. The predicted CT score along with other clinical, laboratory and CT scan image features are then passed to train the various Regression models for predicting the COVID Criticality (CC) Score. These baseline, laboratory and CT features of COVID-19 are reduced using Statistical analysis and Univariate logistic regression analysis. RESULTS: When analysing the prediction of CT scores using images alone, AlexNet+Lasso yields better outcome with regression score of 0.9643 and RMSE of 0.0023 when compared with Decision tree (RMSE of 0.0034; Regression score of 0.9578) and GRU (RMSE of 0.1253; regression score of 0.9323). When analysing the prediction of CC scores using CT scores and other baseline, laboratory and CT features, VGG-16+Linear Regression yields better results with regression score of 0.9911 and RMSE of 0.0002 when compared with Linear SVR (RMSE of 0.0006; Regression score of 0.9911) and LSTM (RMSE of 0.0005; Regression score of 0.9877). The correlation analysis is performed to identify the significance of utilizing other features in prediction of CC Score. The correlation coefficient of CT scores with actual value is 0.93 and 0.92 for Early stage group and Critical stage group respectively. The correlation coefficient of CC scores with actual value is 0.96 for Early stage group and 0.95 for Critical stage group.The classification of COVID-19 patients are carried out with the help of predicted CC Scores. CONCLUSIONS: This proposed work is carried out in the motive of helping radiologists in faster categorization of COVID patients as Early or Severe staged using CC Scores. The automated prediction of COVID Criticality Score using our diagnostic model can help radiologists and physicians save time for carrying out further treatment and procedures.

Prediction of COVID-19 with Computed Tomography Images using Hybrid Learning Techniques.

Reverse Transcription Polymerase Chain Reaction (RT-PCR) used for diagnosing COVID-19 has been found to give low detection rate during early stages of infection. Radiological analysis of CT images has given higher prediction rate when compared to RT-PCR technique. In this paper, hybrid learning models are used to classify COVID-19 CT images, Community-Acquired Pneumonia (CAP) CT images, and normal CT images with high specificity and sensitivity. The proposed system in this paper has been compared with various machine learning classifiers and other deep learning classifiers for better data analysis. The outcome of this study is also compared with other studies which were carried out recently on COVID-19 classification for further analysis. The proposed model has been found to outperform with an accuracy of 96.69%, sensitivity of 96%, and specificity of 98%.

Detection of COVID-19 using CXR and CT images using Transfer Learning and Haralick features.

Recognition of COVID-19 is a challenging task which consistently requires taking a gander at clinical images of patients. In this paper, the transfer learning technique has been applied to clinical images of different types of pulmonary diseases, including COVID-19. It is found that COVID-19 is very much similar to pneumonia lung disease. Further findings are made to identify the type of pneumonia similar to COVID-19. Transfer Learning makes it possible for us to find out that viral pneumonia is same as COVID-19. This shows the knowledge gained by model trained for detecting viral pneumonia can be transferred for identifying COVID-19. Transfer Learning shows significant difference in results when compared with the outcome from conventional classifications. It is obvious that we need not create separate model for classifying COVID-19 as done by conventional classifications. This makes the herculean work easier by using existing model for determining COVID-19. Second, it is difficult to detect the abnormal features from images due to the noise impedance from lesions and tissues. For this reason, texture feature extraction is accomplished using Haralick features which focus only on the area of interest to detect COVID-19 using statistical analyses. Hence, there is a need to propose a model to predict the COVID-19 cases at the earliest possible to control the spread of disease. We propose a transfer learning model to quicken the prediction process and assist the medical professionals. The proposed model outperforms the other existing models. This makes the time-consuming process easier and faster for radiologists and this reduces the spread of virus and save lives.