Hybrid Optimization with Recurrent Neural Networkbased 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 preprocessing
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.