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_23
Snippet: • After processing all the images and converting them into features and using SMOTE for intra-class variations, the next step involves fitting the dataset using different machine learning predictive classifiers. For this purpose, We have integrated Logistic Regression (LR) 25 , k-Nearest Neighbour (kNN) 26 , Decision Trees (DT) 27 , Random Forest (RF) 28 , Adaptive Boosting (AdaBoost) 29 , Naive Bayes (NB) 30 and XGBoost(XGB) 31 to classify the.....
Document: • After processing all the images and converting them into features and using SMOTE for intra-class variations, the next step involves fitting the dataset using different machine learning predictive classifiers. For this purpose, We have integrated Logistic Regression (LR) 25 , k-Nearest Neighbour (kNN) 26 , Decision Trees (DT) 27 , Random Forest (RF) 28 , Adaptive Boosting (AdaBoost) 29 , Naive Bayes (NB) 30 and XGBoost(XGB) 31 to classify the COVID-19, Normal, and Pneumonia (shown in the Table 2 ).
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