Introduction: Stroke is a major catastrophic event majorly defining morbidity and mortality in patients. The assessment of quality of life after stroke is becoming common with the recognition that evaluation of treatment should include quality as well as quantity of survival. Materials and Methods: The current prospective, observational study of six months duration was performed to assess the quality of life in stroke inpatients of a tertiary care teaching hospital in south India using SSQoL scale and Barthel Index. The study participants were interviewed based on 12 domains and 49 items of SSQoL scale and 10 questions from Barthel index. Results: Out of 48 stroke inpatients, 62.5% were male and 37.5% female, furthermore there was equal distribution of patients with respect to gender within age groups of 56-60 and 61-65 years. The changes in quality of life observed in all stroke inpatients using SSQoL scale and Barthel Index with respect to associated complications was found to be higher in hypertension populations, patients with tobacco and pan chewing habit was observed as primary risk factor for stroke attacks, and patients with 1 st episode without accident proved more risk for stroke attack. Conclusions: In conclusion, improved methods to measure the quality of life in stroke are required. Quality of life measures must be valid, reliable, responsive and comprehensive; and also with the involvement of patients at every stage of measure. Performing an exemplary and extensive research studies on stroke and its quality of life could bring better health outcome in individual patients.

Background: Accurate semantic segmentation of kidney tumors in computed tomography (CT) images is difficult because tumors feature varied forms and occasionally, look alike. The KiTs19 challenge sets the groundwork for future advances in kidney tumor segmentation. Methods: We present weight pruning (WP)-UNet, a deep network model that is lightweight with a small scale; it involves few parameters with a quick assumption time and a low floating-point computational complexity. Results: We trained and evaluated the model with CT images from 210 patients. The findings implied the dominance of our method on the training Dice score (0.98) for the kidney tumor region. The proposed model only uses 1,297,441 parameters and 7.2e floating-point operations, three times lower than those for other network models. Conclusions: The results confirm that the proposed architecture is smaller than that of UNet, involves less computational complexity, and yields good accuracy, indicating its potential applicability in kidney tumor imaging.

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