Intelligent optimization-based pulmonary emphysema detection with adaptive multi-scale dilation assisted residual network with Bi-LSTM layer

Abstract

Emphysema is a type of chronic disorder that occurs due to the abnormal expansion of the alveoli in the lungs. This causes several difficulties to breathe, and the final stage of emphysema leads to lung cancer. By using pulmonary function tests and Computed Tomography (CT) scans, the progressive destruction of emphysema disease can be addressed. The early-stage detection of emphysema may help to reduce the risk of the patients. The primary diagnosis should be carried out with the help of CT and spirometry analysis to reduce mortality rates. In the computer vision-based approaches, the inter-observer and intra-observer variations are difficult to analyze. To overcome these complications, an enhanced emphysema disease detection model with adaptive deep-structured architecture is implemented. At first, the input images are undergone, where histogram equalization, pre-processing stage, filtering technique, and Contrast Limited Adaptive Histogram Equalization methods are utilized. Then, the pre-processed images are fed into the lung segmentation process by using Fuzzy C-Means Clustering (FCM) and Adaptive Region Growing. Those segmented images are provided as input for the proposed emphysema detection by utilizing Adaptive Multi-Scale Dilation Assisted Residual Network with Bi-LSTM Layer (AMSD-RN-Bi-LSTM) layer, in which the constraints are optimized by using Improved Honey Badger Algorithm (IHBA). Through the experimental analysis, the proposed pulmonary emphysema detection model shows the tendency to give a rapid diagnosis of a disease that aids to identify the disease and diagnose them effectively.