Lung disease classification in chest X-ray images using optimal cross stage partial bidirectional long short term memory

BackgroundLung disease is the crucial disease that affects the breathing conditions and even causes death. There are various approaches for the lung disease classification; still the inefficiency in accurate detection, computational complexity and over-fitting issues limits the performance of the model. To overcome the challenges, a deep learning model is proposed in this research. Initially, the input is acquired and is pre-processed using three various techniques like data augmentation, filtering and image re-sizing. Then, the threshold based segmentation is employed for obtaining the required region.ObjectiveFrom the segmented image, various categories of lung diseases like COVID, lung Opacity, Pneumonia and normal are identified using the proposed Optimal Cross Stage Partial Bidirectional Long short term memory (OCBiNet).MethodsThe proposed OCBiNet is designed using Bidirectional Long short-term memory (BiNet) with Cross Stage Partial connection in its hidden state. Besides, the adjustable parameters are modified using the proposed Improved Mother Optimization (ImMO) algorithm.ResultsThe ImMO algorithm is designed by integrating the Logistic Chaotic Mapping within the conventional Mother Optimization algorithm for enhancing the convergence rate in obtaining the global best solution.ConclusionsThe proposed OCBiNet is evaluated based on Accuracy, Recall, Precision, and F-Score and acquired the values of 99.11%, 98.98%, 99.18%, and 99.08% respectively.