DEEP LEARNING BASED SYSTEM AND METHOD FOR AUTOMATIC IDENTIFICATION OF COVID-19 REGIONS ON CT-IMAGES

OF THE INVENTION The present invention is to detect the COVID-19 using a novel dual-branch combination network. The detection of lung infection automatically from the lung CT images affords the high potential to improve the conventional healthcare approach for dealing with COVID-19. However, CT- image-based detection of Covid-19 has confronted substantial challenges like maximum variation in the characteristics of the infection, less sensitivity, and less depth variation amidst infectious and regular tissues. Furthermore, it is complex to gather a huge amount of data in a short period. Subsequently, it is necessary to develop the automated methods for Covid-19 detection to observe the presence of disease from radiological images. To address these shortcomings, a novel dual-branch combination network for the detection of the COVID-19 is enhanced. It can attain classification at the individual level and segmentation of lesions at the same time. Also, a unique lesion attention component was designed to combine the intermediary results of segmentation. In addition, a slice probability mapping technique is introduced to study the transmission from slice to individual stage classification. The proposed technique attained maximum sensitivity, better accuracy, and good interpretability. R.T.Subhalakshmi,Dr.S.Appavu Alias Balamurugan,Dr.S.Sasikala Figures, Tables and Flow charts U-Net based Lesion segmentation segmentation Preprocessing Lesion Attention Covid CT images Classification Slice-level diagnosis Slice proba bility ma pping Dual Branch Combination Network Individual-level diagnosis Performance Analysis Figure 1.Flow of the proposed method