Flower pollination-enhanced CNN for lung disease diagnosis

Abstract

Abstract The utilization of automated software tools is imperative to enhance the efficiency of lung diseases through the analysis of X-ray images. The main objective of this study is to employ an analysis of chest X-ray images to diagnose lung disease. This study presents an Optimized Convolutional Neural Network (CNNFPA) designed to automate the diagnosis of lung disease. The Flower pollination technique is employed to optimize the hyperparameters associated with the training of the layers of the Convolutional Neural Network (CNN). In this paper, a novel model called RCNNFPA model is proposed, which makes use of a pre-trained ResNet50 with its layers frozen. Subsequently, CNNFPA architecture is integrated on top of the frozen ResNet-50 layers. This approach allowed us to leverage the knowledge captured by the ResNet-50 model on a large-scale dataset. To assess the efficacy of the proposed model and perform a comparison study using several classification methodologies, various publicly available datasets comprising images of COVID-19, Viral Pneumonia, Normal, and Tuberculosis are employed. As optimized and elaborated upon in this study, the CNN model is juxtaposed with existing state-of-the-art models. The proposed novel RCNNFPA model demonstrates considerable potential in facilitating the automated screening of individuals affected by different lung diseases.