000			a
999			_c17258 _d17258
003			OSt
005			20220805141822.0
008			220805b xxu||||| |||| 00| 0 eng d
040			_aAIKTC-KRRC _cAIKTC-KRRC
100			_917465 _aSingh, Tismeet
245			_aEarly detection of covid-19 using machine learning
250			_aVol.7 (01), Jan-Feb
260			_aNew Delhi _c2022 _bAssociated Management Consultants
300			_a8-24p.
520			_aThe COVID-19 Pandemic had a devastating impact both on social and economic fronts for a majority of the countries around the world. It spread at an exponential rate and affected millions of people across the globe. The aim of this study was to improve upon a lot of existing studies on COVID detection using Machine Learning. While Machine Learning methods have been widely used in other medical domains, there is now considerable demand for ML-guided diagnostic systems for screening, tracking, analysing, and predicting the spread of COVID-19 and finding a concrete and viable cure for it. We employed the power of Transfer Learning guided Convolutional Networks to predict the existence of the COVID-19 virus in the lung X-Ray of any subject. Deep Learning, one of the most lucrative and potent techniques of machine learning becomes the modern saviour when such crises arise. With the power of this technique, we studied a plethora of models, selected the best ones and then trained them to produce the most optimal results. We used multiple pretrained models and improved upon them by adding structured Dense and Batch Normalisation layers with appropriately selecting activation functions. Elaborate testing yielded a maximum accuracy of over 99%. _b Keywords Computer Vision, Confusion Matrix, Convolutional Neural Network, COVID-19, Deep Learning, Machine Learning, Transfer Learning, X-Ray.
650		0	_94622 _aComputer Engineering
700			_917466 _aAgarwal, Kartikeya
773	0		_x2456-4133 _tIndian Journal of Computer Science _dNew Delhi Associated Management Consultants
942			_2ddc _cAR