Paper Title
Respiratory Disease Prediction Using Deep Learning

Abstract
The medical sector may analyze data with Convolutional Neural Networks (CNN) at extremely fast speeds without compromising accuracy. As a result of the dearth of samples for lung diseases, it is challenging to predict respiratory diseases with any level of certainty. Traditional supervised machine learning algorithms do not yield more accurate results when taught with fewer data samples. A deep learning-based respiratory illness prediction approach is suggested to categorize COVID-19, pneumonia, and tuberculosis. Data augmentation, Contrast Limited Adaptive Histogram (CLHAE), and Visual Geometry Group are all combined in this method (VGG). The use of integrated data augmentation and the Visual Geometry Group (VGG) model for better illness prediction is the innovative component of this work. Keywords - Deep Learning, Contrast Limited Adaptive Histogram (CLHAE), Visual Geometry Group, (VGG), Disease Detection, Data Augmentation

Author - Kensiya Pillai, Pragya Mishra, Shruti Achary, Shreya Nair, Madhu Nashipudimath

Published : Volume-11,Issue-2  ( Feb, 2024 )

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