2020/04/14 by V. Rajinikanth, Seifedine Kadry, Rajinikanth, Venkatesan +6 · 1 citation
Computer Science · Medicine · #AI in cancer detection #COVID-19 diagnosis using AI #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Radiomics and Machine Learning in Medical Imaging #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2004.09239
openalex publication_date 2020/04/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The pneumonia caused by Coronavirus disease (COVID-19) is one of major global threat and a number of detection and treatment procedures are suggested by the researchers for COVID-19. The proposed work aims to suggest an automated image processing scheme to extract the COVID-19 lesion from the lung CT scan images (CTI) recorded from the patients. This scheme implements the following procedures; (i) Image pre-processing to enhance the COVID-19 lesions, (ii) Image post-processing to extract the lesions, and (iii) Execution of a relative analysis between the extracted lesion segment and the Ground-Truth-Image (GTI). This work implements Firefly Algorithm and Shannon Entropy (FA+SE) based multi-threshold to enhance the pneumonia lesion and implements Markov-Random-Field (MRF) segmentation to extract the lesions with better accuracy. The proposed scheme is tested and validated using a class of COVID-19 CTI obtained from the existing image datasets and the experimental outcome is appraised to authenticate the clinical significance of the proposed scheme. The proposed work helped to attain a mean accuracy of >92% during COVID-19 lesion segmentation and in future, it can be used to examine the real clinical lung CTI of COVID-19 patients.