Chest X-ray image classification using deep belief network with Al-Biruni earth radius and particle swarm optimization

Abstract

Chest X-ray (CXR) is a widely employed radiological clinical assessment tool that provides a quick and effective means of classifying various diseases using CXR images. However, several researchers face challenges with CXR images due to imbalanced datasets and image quality issues. Pre-processing is performed using contrast limited adaptive histogram equalization (CLAHE) to enhance image quality and mitigate noise in the data. The synthetic minority oversampling technique (SMOTE) is applied to create synthetic samples for the minority class and handle class imbalance. The MobileNetV2 performs depth-wise separable convolution is used for feature extraction, while maintaining high efficiency for CXR images. This research proposes a deep belief network (DBN) to classify CXR, which helps capture hierarchical features and complex patterns in CXR images. The combination of particle swarm optimization (PSO) and Al-Biruni earth radius (BER) method is employed for hyperparameter tuning with enhanced DBN classification accuracy. Furthermore, BER is integrated with the PSO algorithm to balance exploration and exploitation while the fitness function is fine-tuned for optimal DBN classification performance. The proposed PSOBER-DBN achieves a high accuracy of 99.86% on the CXR14 dataset, in comparison to existing techniques such as the multi-level residual feature fusion network (MLRFNet).