Publications by authors named "Madhuri Bhavsar"
Article Synopsis
- Deep Learning (DL) models are being effectively used to analyze MRI scans for Alzheimer's Disease (AD), leveraging Cloud Computing to manage computational demands.
- The article provides a systematic tutorial on medical imaging datasets, presenting a case study that compares three DL models: Convolutional Neural Networks (CNN), Visual Geometry Group 16 (VGG-16), and an ensemble approach for AD MRI classification.
- Results indicate that CNN achieved the highest accuracy at 99.285%, while VGG-16 and the ensemble model scored lower, emphasizing the effectiveness of the proposed cloud-based framework for secure and efficient medical image processing.
With the rapid growth in the data and processing over the cloud, it has become easier to access those data. On the other hand, it poses many technical and security challenges to the users of those provisions. Fog computing makes these technical issues manageable to some extent.
View Article and Find Full Text PDFRecently, healthcare stakeholders have orchestrated steps to strengthen and curb the COVID-19 wave. There has been a surge in vaccinations to curb the virus wave, but it is crucial to strengthen our healthcare resources to fight COVID-19 and like pandemics. Recent researchers have suggested effective forecasting models for COVID-19 transmission rate, spread, and the number of positive cases, but the focus on healthcare resources to meet the current spread is not discussed.
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