Differentiating normal and COVID subjects using hough transform based measures in comparison with radon transform - Nalijeni, Tejeshwar Reddy; Ramesh, M.; Sathish, T. (AIP conference proceedings, 2024)
The main aim of this study is to identify the shape variation of lungs which are affected by COVID-19 with the help of Hough Transform and Radon transform features. The dataset is taken from the kaggle open source. A total of 200 image samples are obtained using G-Power, by fixing the parameters such as effect size (d) is given 0.57, standard error rate is given as 0.05 and power is given as 0.80 respectively. These segmented images are used in this analysis for differentiation of normal and COVID subjects using the Radon transform for the shape features extraction. By using SPSS software the significance of the best algorithm is obtained. Shape features which are extracted by Hough transform and Radon transform algorithms are classified by using KNN and SVM classifiers. From the obtained results, the feature values of Radon moments are observed to be statistically significant for M15,7 (p=0.03) compared to Hough transformed moments M10,8 (p=0.06). The mean and standard values of M15,7 for inter (normal and COVID) subjects are (0.51±0.24, 0.10±0.05) for Radon normalised features. The mean and standard deviation values of M10,8 for inter subjects are (0.21±0.04, 0.18±0.03 ) for Hough normalised features. From the above mentioned values, it is clear that the COVID subjects have loss in shape of lungs due to abnormality. The accuracy and F1 score values of Radon transform are 100 and 100 using KNN and SVM classifiers respectively. Conclusion: Therefore, from the above analysis it is clearly observed that Radon transform provides better classification between normal and COVID subjects when compared to Hough transform.