Hybrid Optimization with Recurrent Neural Network-based Medical Image Processing for Predicting Interstitial Lung Disease

Abstract

One of the dreadful diseases that shortens people's lives is lung disease. There are numerous potentially fatal consequences that can arise from interstitial lung disease, such as: Lung hypertension. This illness doesn't influence your overall blood pressure; instead, it only affects the arteries in your lungs. To prevent mortality, it is essential to accurately diagnose pulmonary illness in patients. Various classifiers, including SVM, RF, MLP, and others, are processed to identify lung disorders. Large datasets cannot be processed by these algorithms, which causes false lung disease identification. A combined new Spider Monkey and Lion algorithm is suggested as a solution to get around these limitations. Images of interstitial lung disease (ILD) were taken for the study from the publicly accessible MedGIFT database. The median filter is employed during the pre-processing step of ILD images to reduce noise and remove undesirable objects. The features are extracted using a hybrid spider Monkey and Lion algorithm. The lungs' damaged and unaffected regions are divided into categories using recurrent neural networks. Several metrics such as accuracy, precision, recall, and f1-score are used to evaluate the performance of the proposed system. The results demonstrate that this technique offers more precision, accuracy, and a higher rate of lung illness detection by processing a large number of computerized tomography representations quickly. When compared to other strategies already in use, the proposed model's accuracy is greater at 99.8%. This method could be beneficial for staging the severity of interstitial lung illness, prognosticating, and forecasting treatment outcomes and survival, determining risk control, and allocation of resources.