Advanced Detection of COVID-19 Through X-ray Imaging using CovidFusionNet with Hybrid CNN Fusion and Multi-resolution Analysis

Abstract

The rapid diagnosis of COVID-19 through imaging is crucial in the current pandemic scenario. This study introduces the CovidFusionNet, a novel model adapted for efficient COVID-19 image classification. By effectively combining fusing features from seven pre-trained convolutional neural networks (CNNs), our model presents better accuracy in detecting COVID-19 from X-ray images. Three separate datasets, obtained from Kaggle, were used in this study to ensure the reliability and robustness of the model. The Continuous and Discrete Wavelet Transform was implemented for robust multi-resolution image analysis to maintain image properties after denoising. A novel enhancement method was also proposed, combining the capabilities of Adaptive Histogram Equalization (AHE) and Wavelet Transforms to emphasize finer details and concurrently heighten clarity while minimizing noise. Furthermore, to mitigate class imbalance, an oversampling approach was implemented. Comprehensive validation using 12 metrics across each dataset verified the proposed consistent performance, with remarkable accuracies of 98.02% for Dataset One, 99.30% for Dataset Two, and 98.25% for Dataset Three. Comparing CovidFusionNet against seven well-known pre-trained models showed that CovidFusionNet appeared more capable. This research advances the area of image-based diagnosis using COVID-19 and provides a model for quick medical actions.