Improved Transfer Learning Architecture to Classify Covid-19 Affected Chest X-Rays using Noisy Student Pre-training

ABSTRACT

During this pandemic situation Chest, X-rays may play a vital role in the diagnosis of COVID-19. The shortage of labeled medical images becomes this diagnosis more challenging. We established an efficient transfer learning method for classifying COVID-19 chest X-rays. We also gathered images from the publicly available chest x-ray datasets. We built an effective classifier for our pre-trained model with the latest state-of-the-art activation function Mish, Batch Normalization, and Dropout Layer. Our classifier efficiently detects Covid-19, Pneumonia, and normal case by differentiating inflammation in the lungs. Furthermore, we used the recent state-of-the-art idea of semi-supervised Noisy Student Training in our EfficientNet Architecture model and compared it with other benchmark models. We found that our proposed model performs well by using benchmark evaluation metrics(accuracy, F1 score, and ROC(AUC)) and our ROC(AUC) score of 98% overall. After that, we visually interpreted our training model with a saliency map to make it more understandable. Contribution We contributed an improved three-class classifier part using the new state-of-the-art activation function Mish for the EfficientNet Transfer Learning model and improved the accuracy of Covid-19 Classification through Semi-Supervised Noisy Student training. © 2021 IEEE