Intelligent Medical Image Processing

Title: Screening of COVID-19 based on the extracted radiomics features from chest CT images

Abstract:

Radiomics has been widely used in quantitative analysis of medical images for disease diagnosis and prognosis assessment. The objective of this study is to test a machine-learning (ML) method based on radiomics features extracted from chest CT images for screening COVID-19 cases. The study is carried out on two groups of patients, including 138 patients with confirmed and 140 patients with suspected COVID-19. We focus on distinguishing pneumonia caused by COVID-19 from the suspected cases by segmentation of whole lung volume and extraction of 86 radiomics features. Followed by feature extraction, nine featureselection procedures are used to identify valuable features. Then, ten ML classifiers are applied to classify and predict COVID-19 cases. Each ML models is trained and tested using a ten-fold cross-validation method. The predictive performance of each ML model is evaluated using the area under the curve (AUC) and accuracy. The range of accuracy and AUC is from 0.32 (recursive feature elimination [RFE]+Multinomial Naive Bayes [MNB] classifier) to 0.984 (RFE+bagging [BAG], RFE+decision tree [DT] classifiers) and 0.27 (mutual information [MI]+MNB classifier) to 0.997 (RFE+k-nearest neighborhood [KNN] classifier), respectively. There is no direct correlation among the number of the selected features, accuracy, and AUC, however, with changes in the number of the selected features, the accuracy and AUC values will change. Feature selection procedure RFE+BAG classifier and RFE+DT classifier achieve the highest prediction accuracy (accuracy: 0.984), followed by MI+Gaussian Naive Bayes (GNB) and logistic regression (LGR)+DT classifiers (accuracy: 0.976). RFE+KNN classifier as a feature selection procedure achieve the highest AUC (AUC: 0.997), followed by RFE+BAG classifier (AUC: 0.991) and RFE+gradient boosting decision tree (GBDT) classifier (AUC: 0.99). CONCLUSION: This study demonstrates that the MLmodel based on RFE+KNN classifier achieves the highest performance to differentiate patients with a confirmed infection caused by COVID-19 from the suspected cases.

Title: Detecting COVID-19 in chest images based on deep transfer learning and machine learning algorithms

Abstract:

Background: This study aimed to propose an automatic prediction of COVID-19 disease using chest CT images based on deep transfer learning models and machine learning (ML) algorithms. The dataset consisted of 5480 samples in two classes, including 2740 CT chest images of patients with confirmed COVID-19 and 2740 images of suspected cases was assessed. The DenseNet201 model has obtained the highest training with an accuracy of 100%. In combining pre-trained models with ML algorithms, the DenseNet201 model and KNN algorithm have received the best performance with an accuracy of 100%. Created map by t-SNE in the DenseNet201 model showed not any points clustered with the wrong class. The mentioned models can be used in remote places, in low- and middle-income countries, and laboratory equipment with limited resources to overcome a shortage of radiologists

Title: Predicting breast cancer response to neoadjuvant chemotherapy using ensemble deep transfer learning based on CT images

Abstract:

To develop an ensemble a deep transfer learning model of CT images for predicting pathologic complete response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). METHODS: The data were obtained from the public dataset ‘QIN-Breast’ from The Cancer Imaging Archive (TCIA). CT Images were gathered before and after the first cycle of NAC. CT images of 121 breast cancer patients were used to train and test the model. Among these patients, 58 achieved a pCR and 63 showed a non-pCR based pathology examination of surgical results after NAC. The dataset was split into training and testing subsets with a ratio of 7:3. In addition, the number of training samples in the dataset was increased from 656 to 1,968 by performing an image augmentation method. Two deep transfer learning models namely, DenseNet201 and ResNet152V2, and the ensemble model with a concatenation of two models, were trained and tested using CT images. The ensemble model obtained the highest accuracy of 100% on the testing dataset. Furthermore, we received the best performance of 100% in recall, Precision and f1-score value for the ensemble model. This supports the fact that the ensemble model results in better-generalized model and leads to efficient framework. Although a 0.004 and 0.003 difference were seen between the AUC of two base models (DenseNet201 and ResNet152V2) and the proposed ensemble, this increase in the model quality is critical in medical research. T-SNE revealed that in the proposed ensemble, no points were clustered into the wrong class. These results expose the strong performance of the proposed ensemble. The study concluded that the ensemble model can increase the ability to predict breast cancer response to first-cycle NAC than two DenseNet201 and ResNet152V2 models.

Title: A machine learning method based on lesion segmentation for quantitative analysis of CT radiomics to detect COVID–19

Abstract:

Coronavirus disease (COVID-19) since late December 2019 became an epidemic all over the world so that widely spread throughout. Computed tomography (CT) imaging can be effective in isolating the infected persons and controlling this epidemic. Radiomics is an image quantitative analysis procedure that can quantify imaging by extracting specific features from CT images. We aimed to develop a machine learning (ML) method based lesion segmentation for quantitative analysis of CT radiomics to detect COVID–19. The current study was carried out on two groups of patients including 98 patients with confirmed COVID-19 and 96 with suspected COVID-19. A total of 755 radiomics features were extracted, including 594 gray level co-occurrence matrix features (GLCM), 56 intensity direct features, 49 intensity histogram features, 33 gray level run length matrix features (GLRLM), 18 shape features, and 5 neighbor intensity difference features. Two feature selection procedures including Pearson Correlation (PC) and Recursive Feature Elimination (RFE) were used. As well as, we examined three classifiers including Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbor (KNN). The performance of the feature selection and classification procedures was obtained using 6 criteria. We have obtained the best performance as the accuracy of 98%, recall of 99%, and the area under the curve (AUC) value of 100% for the feature selection procedure RFE and RF classifier. As a result, it can be concluded that the radiomics features of the lung lesions based on ML can be used to differentiate COVID-19 patients.