Multiclass Classification of Chest X Ray Images Using Resnet-152 with Mix-Up Regularization Method

For the past few decades, thoracic disease classification using Chest X-Ray images has remained challenging in Artificial Intelligence (AI).The traditional Deep CNNs (DCNNs) always predict with overconfidence, so the accuracy of the architectures is likely to be lower than predictive scores.Also, DCNNs suffer from overfitting due to the large number of parameters, and they require immense data augmentation to reduce overfitting.To overcome these problems, this research implements ResNet-152 with a Mix-Up based regularization method to effectively classify multiclass Chest X-Ray images of the Chest X-ray14 dataset first time.It contains 112,120 Chest X-Ray images across 30,805 patients, with 14 common thoracic disease labels plus one Normal (Healthy) label.The Mix-Up technique creates new samples as a convex combination of training points and disease labels.This Mix-Up-based regularization trains a model by blending image pair and their associated labels.We found that Mix-Up is successful in improving the accuracy of the ResNet-152 model by reducing its overconfidence up to 0.002 and increasing average AUC to 99.2% across all 14 disease classes.The proposed model performed better than ResNet-152 with other regularization methods such as Cut-Out, Cut-Mix, and Aug-Mix Regularization.Compared to the other existing models such as CNN-ELM, PCSANet, and GWSA &LCD models, this model achieved higher average AUC values across all 14 pathologies.