Hierarchical Bayesian optimization based convolutional neural network for chest X-ray disease classification

Abstract

Pneumonia is an infection that affects the lungs, caused by bacteria or viruses inhaled through the air, leading to respiratory problems. The previous researches on this subject have limitations of high dimensional feature subspace and overfitting which minimize the classifier performance. In this research, hierarchical Bayesian optimization based convolutional neural network (HBO-CNN) method is proposed to effectively classify chest X-ray diseases. The proposed HBO algorithm optimizes hyperparameters of CNN which minimizes the overfitting issue and enhances the performance of classification. The hybrid Mexican axolotl optimization (MAO) and tuna swarm optimization (TSO) based feature selection method is used for selecting relevant features for classification that minimizes the high dimensional features. The ResNet 50 method is used for feature extraction to extract hierarchical features from the pre-processed images to differentiate the classes. The proposed HBO-CNN technique is estimated with performance metrics of accuracy, precision, recall, and F1-score. The proposed method attains the highest accuracy 97.95%, precision 92.00%, recall 89.00% and F1-score 92.00%, as opposed to the conventional methods, deep convolutional neural network (DCNN).