TY  - CONF
N2  - During the COVID-19 pandemic, medical imaging techniques like computed tomography (CT) scans have demonstrated effectiveness in combating the rapid spread of the virus. Therefore, it is crucial to conduct research on computerized models for the detection of COVID-19 using CT imaging. A novel processing method has been developed, utilizing radiomic features, to assist in the CT-based diagnosis of COVID-19. Given the lower specificity of traditional features in distinguishing between different causes of pulmonary diseases, the objective of this study is to develop a CT-based radiomics framework for the differentiation of COVID-19 from other lung diseases. The model is designed to focus on outlining COVID-19 lesions, as traditional features often lack specificity in this aspect. The model categorizes images into three classes: COVID-19, non-COVID-19, or normal. It employs enhancement auto-segmentation principles using intensity dark channel prior (IDCP) and deep neural networks (ALS-IDCP-DNN) within a defined range of analysis thresholds. A publicly available dataset comprising COVID-19, normal, and non-COVID-19 classes was utilized to validate the proposed model's effectiveness. The best performing classification model, Residual Neural Network with 50 layers (Resnet-50), attained an average accuracy, precision, recall, and F1-score of 98.8, 99, 98, and 98 respectively. These results demonstrate the capability of our model to accurately classify COVID-19 images, which could aid radiologists in diagnosing suspected COVID-19 patients. Furthermore, our model's performance surpasses that of more than 10 current state-of-the-art studies conducted on the same dataset. © 2023 Institute of Physics Publishing. All rights reserved.
N1  - cited By 0; Conference of 1st International Conference on Electronic and Computer Engineering, ECE 2023 ; Conference Date: 4 July 2023 Through 5 July 2023; Conference Code:196016
KW  - Computerized tomography; Deep neural networks; Diagnosis; Medical imaging; Multilayer neural networks; Network layers; Viruses
KW  -  Auto segmentation; Classification models; Computed tomography scan; Dark channel priors; Lesion segmentations; Medical imaging techniques; Neural-networks; Novel processing; Processing method; Tomography imaging
KW  -  COVID-19
TI  - Auto-Lesion Segmentation with a Novel Intensity Dark Channel Prior for COVID-19 Detection
ID  - scholars18999
AV  - none
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182282415&doi=10.1088%2f1742-6596%2f2622%2f1%2f012002&partnerID=40&md5=14c3dacbb86cc92b644ff715f5fc8e59
A1  - Saleh, B.J.
A1  - Omar, Z.
A1  - Bhateja, V.
A1  - Izhar, L.I.
VL  - 2622
Y1  - 2023///
SN  - 17426588
PB  - Institute of Physics
ER  -