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Background COVID-19 is a novel virus that affects the upper respiratory tract, as well as the lungs. The scale of the global COVID-19 pandemic, its spreading rate, and deaths are increasing regularly. Computed tomography (CT) scans can be used carefully to detect and analyze COVID-19 cases. In CT images/scans, ground-glass opacity (GGO) is found in the early stages of infection. While in later stages, there is a superimposed pulmonary consolidation. Methods This research investigates the quantum machine learning (QML) and classical machine learning (CML) approaches for the analysis of COVID-19 images. The recent developments in quantum computing have led researchers to explore new ideas and approaches using QML. The proposed approach consists of two phases: in phase I, synthetic CT images are generated through the conditional adversarial network (CGAN) to increase the size of the dataset for accurate training and testing. In phase II, the classification of COVID-19/healthy images is performed, in which two models are proposed: CML and QML. Result The proposed model achieved 0.94 precision (Pn), 0.94 accuracy (Ac), 0.94 recall (Rl), and 0.94 F1-score (Fe) on POF Hospital dataset while 0.96 Pn, 0.96 Ac, 0.95 Rl, and 0.96 Fe on UCSD-AI4H dataset. Conclusion The proposed method achieved better results when compared to the latest published work in this domain.

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Objective: To determine the diagnostic accuracy of the placental thickness measured by ultrasound in detecting IUGR babies keeping actual birth weight as the gold standard. Methods: This cross-sectional validation study was conducted in the Department of Radiology P.O.F Hospital Wah Cantt. The data was gathered over a period of six months, from 06-19-2017 to 12-18-2017. A total of 125 patients were included in this study. All pregnant women were examined by the greyscale and Doppler ultrasonography using a color Doppler scanner with a 5.0 MHz convex probe. Placental thickness was measured as the distance between the echogenic line of the chorionic plate and the hypoechoic myometrium. The pregnant females were followed till childbirth and the weight of the baby at birth was recorded. Results: Patients ranged between 20-35 years of age. The average age of the study participants was 27.6±3.3 years, the mean gestational age was 34.2±3.0 weeks, the mean BMI was 23.5±1.3 (kg/m2), and the mean parity was 1.1±1.0. We found a sensitivity of 65.5%, specificity of 83.3%, Positive Predictive Value( PPV) of 98.7%, Negative Predictive Value (NPV) of 10.8%, and diagnostic accuracy of 66.4% for antenatal prediction of IUGR based on placental thickness measurement. Stratification for age and gestational age was also carried out. Conclusion: Placental thickness on ultrasonography can be used as a reliable marker for detecting IUGR babies with an accuracy of 6.4%.