Integrated CNN model for Multi-disease Classification Through Chest X-ray images

Chest disorders are widespread globally, encompassing conditions such as COVID-19, pneumonia, tuberculosis, and fibrosis. The diagnostic process often relies on chest X-ray (CXR) images, given the similarities in symptoms among these diseases. Manual diagnosis is a laborious and challenging endeavor due to the shared characteristics of these disorders. In contrast, leveraging deep learning technologies offers a more efficient and cost-effective approach to analyze CXR images for diagnostic purposes. This paper introduces an integrated model, utilizing both VGG16 and VGG19 architectures, coupled with Principal Component Analysis (PCA) and a feature fusion technique for the classification of multiple diseases. The model encompasses four classes: COVID-19, normal, pneumonia, and tuberculosis, making it suitable for real-time applications. The dataset employed in this study is sourced from the Kaggle repository. Our proposed model achieves an accuracy of 97.50\%, with a training time of approximately 4 seconds. Comparative analyses with other existing models are conducted to validate the effectiveness of the proposed approach.