Analysis Performance Of Image Processing Technique Its Application by Decision Support Systems On Covid-19 Disease Prediction Using Convolution Neural Network

The Covid-19 pandemic has been identified as a key issue for human society, in recent times. The presence of the infection on any human is identified according to different symptoms like cough, fever, headache, breathless and so on. However, most of the symptoms are shared by various other diseases, which makes it challenging for the medical practitioners to identify the infection. To aid the medical practitioners, there are a number of approaches designed which use different features like blood report, lung and cardiac features to detect the disease. The method captures the lung image using magnetic resonance imaging scan device and records the cardiac features. Using the image, the lung features are extracted and from the cardiac graph, the cardiac features are extracted. Similarly, from the blood samples, the features are extracted. By extracting such features from the person, the method estimates different weight measures to predict the disease. Different methods estimate the similarity of the samples in different ways to classify the input sample. However, the image processing techniques are used for different problems in medical domain;the same has been used in the detection of the disease. Also, the presence of Covid-19 is detected using different set of features by various approaches. [ FROM AUTHOR] Copyright of Computer Journal is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Psychological dimension of COVID 19 on farmers in rainfed regions of India

COVID pandemic hit the farming community harder with its nature of infection and also with various restrictions imposed during the first and second lockdown in 2020 and 2021. The present study was conducted during the year 2021 to study the psychological distress experienced by the farmers in wake of COVID-19 in two surrounding districts of Hyderabad, Telangana namely Rangareddy and Vikarabad. Data was collected from 240 farmers from July to September, 2021 just after second wave of COVID-19. Non-specific psychological distress due to pandemic was measured by the Kessler 10 (K10) scale which is a well validated and widely used scale to measure distress. The findings of the study showed that about 76 per cent of the farmers experienced high to very high level of psychological distress during the second wave of COVID 19. This was attributed to the fact that these districts were dependent on Hyderabad for sale of its produce which was badly affected by pandemic leading to distress. The results further revealed that majority of farmers (85%) involved in full time farming were experiencing very high distress while only 15 per cent farmers having service in addition to farming were in the same category. This highlights the need of providing alternate livelihood strategies for farmers to tide over uncertain circumstances.

Analysis Performance Of Image Processing Technique Its Application by Decision Support Systems On Covid-19 Disease Prediction Using Convolution Neural Network

The Covid-19 pandemic has been identified as a key issue for human society, in recent times. The presence of the infection on any human is identified according to different symptoms like cough, fever, headache, breathless and so on. However, most of the symptoms are shared by various other diseases, which makes it challenging for the medical practitioners to identify the infection. To aid the medical practitioners, there are a number of approaches designed which use different features like blood report, lung and cardiac features to detect the disease. The method captures the lung image using magnetic resonance imaging scan device and records the cardiac features. Using the image, the lung features are extracted and from the cardiac graph, the cardiac features are extracted. Similarly, from the blood samples, the features are extracted. By extracting such features from the person, the method estimates different weight measures to predict the disease. Different methods estimate the similarity of the samples in different ways to classify the input sample. However, the image processing techniques are used for different problems in medical domain;the same has been used in the detection of the disease. Also, the presence of Covid-19 is detected using different set of features by various approaches.

Lumbar Spine Surgery and What We Lost in the Era of the Coronavirus Pandemic: A Survey of the Lumbar Spine Research Society.

STUDY DESIGN: This was a survey of the surgeon members of the Lumbar Spine Research Society (LSRS). OBJECTIVE: The purpose of this study was to assess trends in surgical practice and patient management involving elective and emergency surgery in the early months of the coronavirus pandemic. SUMMARY OF BACKGROUND DATA: The novel coronavirus has radically disrupted medical care in the first half of 2020. Little data exists regarding the exact nature of its effect on spine care. METHODS: A 53-question survey was sent to the surgeon members of the LSRS. Respondents were contacted via email 3 times over a 2-week period in late April. Questions concentrated on surgical and clinical practice patterns before and after the pandemic. Other data included elective surgical schedules and volumes, as well as which emergency cases were being performed. Surgeons were asked about the status of coronavirus disease 2019 (COVID-19) virus testing. Circumstances for performing surgical intervention on patients with and without testing as well as patients testing positive were explored. RESULTS: A total of 43 completed surveys were returned of 174 sent to active surgeons in the LSRS (25%). Elective lumbar spine procedures decreased by 90% in the first 2 months of the pandemic, but emergency procedures did not change. Patients with "stable" lumbar disease had surgeries deferred indefinitely, even beyond 8 weeks if necessary. In-person outpatient visits became increasingly rare events, as telemedicine consultations accounted for 67% of all outpatient spine appointments. In total, 91% surgeons were under some type of confinement. Only 11% of surgeons tested for the coronavirus on all surgical patients. CONCLUSIONS: Elective lumbar surgery was significantly decreased in the first few months of the coronavirus pandemic, and much of outpatient spine surgery was practiced via telemedicine. Despite these constraints, spine surgeons performed emergency surgery when indicated, even when the COVID-19 status of patients was unknown. LEVEL OF EVIDENCE: Level IV.