An enhanced approach for chest X-ray image classification via residual EfficientNet-based LSTM layer with adaptive heuristic assisted segmentation for the early detection of diseases

In this research study, a CXR image classification model is developed to automatically detect the disease at an earlier stage. In the beginning phase, the necessary CXR images are accumulated from online resources. Further, the collected images are transferred to the second phase to execute the segmentation process. In this phase, a novel structure called Adaptive RefineNet (ARNet) is recommended for segmenting the collected CXR images. The ARNet-based segmentation process is helped to analyze different sizes of abnormalities in the CXR images. The segmentation performance of the ARNet is enhanced by fine-tuning the parameters by Augmented Language Education Optimization (ALEO). The segmented images from the ARNet are given to the final phase for performing the classification using the Residual EfficientNet with LSTM layer (RE-LSTM). Finally, simulation analysis is conducted on the developed method to prove the model's efficiency in the CXR image classification process.