scholarly journals Automatic classification of severity of COVID ‐19 patients using texture feature and random forest based on computed tomography images

2021 ◽  
Author(s):  
Nasrin Amini ◽  
Ahmad Shalbaf
Keyword(s):  
Computed Tomography ◽  
Random Forest ◽  
Texture Feature ◽  
Automatic Classification ◽  
Computed Tomography Images

scholarly journals Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 893
Author(s):  
Yazan Qiblawey ◽  
Anas Tahir ◽  
Muhammad E. H. Chowdhury ◽  
Amith Khandakar ◽  
Serkan Kiranyaz ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Early Stage ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Lung Region ◽  
Computed Tomography Images ◽  
Feature Pyramid ◽  
Severity Levels

Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images. An extensive set of experiments were performed using Encoder–Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature Pyramid Network (FPN), with different backbone (encoder) structures using the variants of DenseNet and ResNet. The conducted experiments for lung region segmentation showed a Dice Similarity Coefficient (DSC) of 97.19% and Intersection over Union (IoU) of 95.10% using U-Net model with the DenseNet 161 encoder. Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94.13% and IoU of 91.85% using the FPN with DenseNet201 encoder. The proposed system can reliably localize infections of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. Moreover, the proposed system achieved high COVID-19 detection performance with 99.64% sensitivity and 98.72% specificity. Finally, the system was able to discriminate between different severity levels of COVID-19 infection over a dataset of 1110 subjects with sensitivity values of 98.3%, 71.2%, 77.8%, and 100% for mild, moderate, severe, and critical, respectively.

scholarly journals Variable Selection from Image Texture Feature for Automatic Classification of Concrete Surface Voids

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ziting Zhao ◽  
Tong Liu ◽  
Xudong Zhao
Keyword(s):  
Variable Selection ◽  
Gabor Filter ◽  
Texture Feature ◽  
Concrete Surface ◽  
Automatic Classification ◽  
Ensemble Classification ◽  
Image Texture ◽  
Inspection Time ◽  
Selection Strategy

Machine learning plays an important role in computational intelligence and has been widely used in many engineering fields. Surface voids or bugholes frequently appearing on concrete surface after the casting process make the corresponding manual inspection time consuming, costly, labor intensive, and inconsistent. In order to make a better inspection of the concrete surface, automatic classification of concrete bugholes is needed. In this paper, a variable selection strategy is proposed for pursuing feature interpretability, together with an automatic ensemble classification designed for getting a better accuracy of the bughole classification. A texture feature deriving from the Gabor filter and gray-level run lengths is extracted in concrete surface images. Interpretable variables, which are also the components of the feature, are selected according to a presented cumulative voting strategy. An ensemble classifier with its base classifier automatically assigned is provided to detect whether a surface void exists in an image or not. Experimental results on 1000 image samples indicate the effectiveness of our method with a comparable prediction accuracy and model explicable.

scholarly journals Diagnosis of COVID-19 Infection Using Three-Dimensional Semantic Segmentation and Classification of Computed Tomography Images

2021 ◽  
Vol 68 (2) ◽  
pp. 2451-2467
Author(s):  
Javaria Amin ◽  
Muhammad Sharif ◽  
Muhammad Almas Anjum ◽  
Yunyoung Nam ◽  
Seifedine Kadry ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Computed Tomography Images

Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

2015 ◽  
Vol 26 (1) ◽  
pp. 195-202 ◽  
Author(s):  
Francesco Ciompi ◽  
Bartjan de Hoop ◽  
Sarah J. van Riel ◽  
Kaman Chung ◽  
Ernst Th. Scholten ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Convolutional Neural Network ◽  
Automatic Classification
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