Automatic Detection of COVID-19 in the Lungs X-ray Images using Pre-trained Deep Learning Model CNN

COVID-19 is a highly contagious epidemic, and detection in the incipient phase is essential to curb the expansion of the disease. Chest X-rays are used in detecting COVID-19 infection. Lung's images and CT -Scan photos are available for coronavirus analysis. This paper is composed of deep learning techniques and methods used to detect COVID-19 contamination in the lung images. The methods employed and collected datasets used for testing metrics are summed up. The Analytical metrics utilized by the methods which are completely comparable. Through this work, we have taken a perspective on COVID-19 affected chest x-ray scanners and healthy patients. After sorting and pre-processing the images and implementing the data addition, we applied deep-learning-based CNN models to compare their performance with other models. The aim is to provide a helping hand to the most distressed medical professionals who are analyzing images with two eyes, detect COVID-19 . According to this analysis we provide a proposed methodology that uses deep learning , dropout technique with python language on Google Colab platform for reduces over-fitting by this deep learning technology. During the testing phase, I got 98.1% accuracy by increasing convolution layer and dropout layer. Our proposed methodology gives better accuracy than other compared models. The primary goal of this paper is to present research on medical image processing and define and implement the proposed CNN model.