Enhancing lung diseases recognition through CNN-RNN methodologies

Abstract

Diagnostics of respiratory disorders greatly benefit from medical imaging, especially X-ray imaging, which offers important information about the anatomical anomalies of the lungs. As we explore deeper into the field of lung illness recognition, it becomes clear that using multiscale Deep Convolutional Neural Network techniques has the potential to transform the detection of pneumonia and tuberculosis from Xray pictures. In this paper, we will classify images through a process that requires only chest-xray images. We have proposed a Deep Learning (DL) based algorithm for lung disease detection, which we termed as Convolutional Recurrent Network (CRNet). In our research, we classify chest X-ray images into four categories according to the publicly available dataset. Our proposed model can calculate the dependency and continuity properties of the intermediate layer output very precisely. At the same time, the features of these intermediate layers can be combined with the final fully-connected network for classification prediction, resulting in better classification accuracy. We have explored the potential of combining CNN and RNN with XAI to identify lung diseases from chest radiographs to improve diagnostic accuracy compared to traditional single-scale methods. Upon comparing our suggested model with the current models, we discovered that, with an accuracy of 93.51% on the full dataset, our suggested model achieved the best accuracy of all the architectures we compared. Moreover, our suggested model C-RNet was observed to accurately categorize and detect the regions of disease through approaches such as Grad-CAM.