ABSTRACT
In the Covid-19 pandemic situation, the world is looking for immunity-boosting techniques for fighting against coronavirus. Every plant is medicine in one or another way, but Ayurveda explains the uses of plant-based medicines and immunity boosters for specific requirements of the human body. To help Ayurveda, botanists are trying to identify more species of medicinal immunity-boosting plants by evaluating the characteristics of the leaf. For a normal person, detecting immunity-boosting plants is a difficult task. Deep learning networks provide highly accurate results in image processing. In the medicinal plant analysis, many leaves are like each other. So, the direct analysis of leaf images using the deep learning network causes many issues for medicinal plant identification. Hence, keeping the requirement of a method at large to help all human beings, the proposed leaf shape descriptor with the deep learning-based mobile application is developed for the identification of immunity-boosting medicinal plants using a smartphone. SDAMPI algorithm explained numerical descriptor generation for closed shapes. This mobile application achieved 96%accuracy for the 64 × 64 sized images.
ABSTRACT
Aim: Objective of this study is to analyze the efficiency of Pseudo Zernike Moment in differentiating COVID subjects from controls compared to Minkowski Functionals. Materials and Methods: The data for this study is obtained from a publicly available dataset. By fixing predefined values to the parameters such as effect size and algorithm power as 0.3 and 0.80 in G power tool provides the required sample size as 176. Pseudo Zernike moments and Minkowski features are extracted from the binary lung CT scans. Result: Pseudo Zernike moment feature (M2) is found to have a mean value of 0.63 for normal subjects and 0.56 for COVID subjects. Minkowski area feature is found to have the ability to differentiate COVID subject compared to its other features. Pseudo Zernike features exhibit better statistical significance (p<0.05) in differentiating normal and COVID subjects. Neural network classifier shows better classification ability with 91% classification accuracy in separating COVID subjects from normal controls. Conclusion: Compared to Minkowski features, pseudo-Zernike moments has better classification ability to differentiate normal and COVID subjects. © 2022 IEEE.
ABSTRACT
Aim: The aim of the analysis is to estimate the deformation in the shape of the lung due to incidence of COVID using pseudo Zernike moments in comparison to invariant moments. Materials and Methods: Images are obtained from Kaggle. Sample size of 176 acquired for the study using G power by considering factors effect size, standard error rate, algorithm power as 0.3, 0.05, 0.80 respectively. In this analysis the classification of normal and COVID subjects is made using seven invariant and pseudo-Zernike moment features. Classification is made using a neural network after extracting the feature values. Result: From the obtained results, the feature values of invariant moments were observed to be statistically significant (p<0.05) than pseudo-Zernike moments. The mean and standard deviation values of variance for normal and COVID subjects were (0.18\± 0.13,0.10± 0.13). For pseudo Zernike's M2 feature statistical values of normal and COVID subjects were (0.63± 0.22,0.56± 0.23). From the values, it is observed that the COVID subjects had loss in shape of lungs due to abnormality. Variance, skewness and kurtosis were found to be statistically significant in differentiating normal and COVID subjects. The accuracy and F1 score values of invariant moments were 0.98 and 0.97 respectively. Conclusion: Therefore, from this analysis it is observed that invariant moments provide significantly better classification between normal and COVID subjects when compared to pseudo Zernike moments. © 2022 IEEE.