Auto-Lesion Segmentation with a Novel Intensity Dark Channel Prior for
  COVID-19 Detection
Basma Jumaa Saleh Zaid Omar Vikrant Bhateja Lila Iznita Izhar
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.