Diagnosis of Covid-19 from CT slices using Whale Optimization Algorithm, Support Vector Machine and Multi-Layer Perceptron.

BACKGROUND: The coronavirus disease 2019 is a serious and highly contagious disease caused by infection with a newly discovered virus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). OBJECTIVE: A Computer Aided Diagnosis (CAD) system to assist physicians to diagnose Covid-19 from chest Computed Tomography (CT) slices is modelled and experimented. METHODS: The lung tissues are segmented using Otsu's thresholding method. The Covid-19 lesions have been annotated as the Regions of Interest (ROIs), which is followed by texture and shape extraction. The obtained features are stored as feature vectors and split into 80:20 train and test sets. To choose the optimal features, Whale Optimization Algorithm (WOA) with Support Vector Machine (SVM) classifier's accuracy is employed. A Multi-Layer Perceptron (MLP) classifier is trained to perform classification with the selected features. RESULTS: Comparative experimentations of the proposed system with existing eight benchmark Machine Learning classifiers using real-time dataset demonstrates that the proposed system with 88.94% accuracy outperforms the benchmark classifier's results. Statistical analysis namely, Friedman test, Mann Whitney U test and Kendall's Rank Correlation Coefficient Test has been performed which indicates that the proposed method has a significant impact on the novel dataset considered. CONCLUSION: The MLP classifier's accuracy without feature selection yielded 80.40%, whereas with feature selection using WOA, it yielded 88.94%.

Chemo-profiling of Purpureocillium lilacinum and Paecilomyces variotii isolates using GC-MS analysis, and evaluation of their metabolites against M. incognita.

Nematophagous fungi are the best alternatives to chemical nematicides for managing nematodes considering environmental health. In the current study, activity of metabolites from ten isolates of Purpureocillium lilacinum (Thom) Luangsa-ard (Hypocreales: Ophiocordycipitaceae) and two isolates of Paecilomyces variotii Bainier (Eurotiales: Trichocomaceae), were examined to inhibit the hatching of Meloidogyne incognita (Kofoid & White) Chitwood (Tylenchida: Heteroderidae) eggs. At 100%, 50%, and 25% concentrations, respectively, the culture filtrate of the isolate P. lilacinum 6887 prevented 97.55%, 90.52%, and 62.97% of egg hatching. Out of all the isolates, Pl 6887, Pl 6553, and Pl 2362 showed the greatest results in the hatching inhibition experiment.Gas chromatography-mass spectrometry (GC-MS) analysis revealed a variety of nematicidal compounds from different isolates. A total of seven nematicidal compounds, including four very potent nematicidal fatty acids were found in the isolate Pl 6553. Secondary metabolites of the same isolate possess the highest M. incognita juvenile mortality, i.e., 43.33% and 92% after 48 hrs of treatment at 100 and 200 ppm concentrations, respectively. Significant difference was observed in juvenile mortality percentage among the isolate having highest and lowest nematicidal compounds. Nematicidal fatty acids like myristic and lauric acid were found for the first time in P. lilacinum. Multiple vacuole-like droplets were found inside the unhatched eggs inoculated with the culture filtrate of isolate Pl 6887, and also in the juveniles that perished in the ethyl acetate extract of isolate Pl 6553.