DETECTION OF COVID-19 FROM CHEST X- RAY IMAGES USING MACHINE LEARNING

Novel corona virus is fast spreading pathogen worldwide and is threatening billions of lives. SARS n-CoV2 is known to affect the lungs of the COVID- 19 positive patients. Chest x-rays are the most widely used imaging technique for clinical diagnosis due to fast imaging time and low cost. The purpose of this study is to use deep learning technique for automatic detection of COVID-19 using chest x-rays. Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. Thus, transfer learning, an effective mechanism that can provide a promising solution by transferring knowledge from generic object recognition tasks to domain-specific tasks. The main objective is to predictive analytics model to diagnose malignant or benign with selecting the highest accuracy result of supervised machine learning algorithm to improvise the prediction. Additionally, discuss the performance from the given hospital dataset with evaluation classification report and identify the confusion matrix. The data validation, data cleaning/preparing and data visualization will be done on the entire given dataset and the result shows that the effectiveness of the proposed machine learning algorithm technique can be compared with best accuracy with precision, Recall and F1 Score.