Enhanced Image Processing for Improved Detection and Analysis of COVID-19 in Medical Imaging by U sing Machine Learning Algorithms

This work intends to increase COVID-19 identification and analysis utilizing modern image processing methods and machine learning algorithms. We deployed a dataset including 5,000 chest X-ray pictures from the COVID-19 Image Data Collection and performed numerous preprocessing methods, including noise reduction and normalization, to prepare the data. For machine learning, we deployed a convolutional neural network (CNN) with a model architecture of 3 convolutional layers and 2 dense layers. Our CNN model obtained an outstanding accuracy of 94.7% in recognizing COVID-19 patients from the X-ray pictures. This high accuracy illustrates the efficiency of merging modern image processing techniques with machine learning to increase the diagnostic precision for COVID-19, possibly assisting in more rapid and precise illness identification.