Author: Rahul Kumar; Ridhi Arora; Vipul Bansal; Vinodh J Sahayasheela; Himanshu Buckchash; Javed Imran; Narayanan Narayanan; Ganesh N Pandian; Balasubramanian Raman
Title: Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers Document date: 2020_4_17
ID: 59ghorzf_8
Snippet: In this section, the results for the proposed methodology is discussed. Table 2 depicts the results of the final classification metrics produced by the proposed methodology. As can be seen in the table, many potential classifiers are listed which have been utilized for the performance calculation of the classification task. It can be inferred that the metrics corresponding to the Random Forest (RF) classifier and the XGBoost (XGB) classifier outp.....
Document: In this section, the results for the proposed methodology is discussed. Table 2 depicts the results of the final classification metrics produced by the proposed methodology. As can be seen in the table, many potential classifiers are listed which have been utilized for the performance calculation of the classification task. It can be inferred that the metrics corresponding to the Random Forest (RF) classifier and the XGBoost (XGB) classifier outperformed from the rest indicating that they have a better understanding of the image features which were input to them.
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