Intelligent Deep Learning System for Enhanced Pulmonary Disease Diagnosis Through Five-Class Mode

The respiratory system's diseases, including many disorders that impair lung function and cause respiratory distress, are a significant public health issue.Many other etiological variables contribute to these disorders, such as genetic predisposition, smoking, infections, and exposure to environmental risks.To lessen the effects of these illnesses, prompt diagnosis and efficient therapy approaches are essential.This work presents a sophisticated lung disease diagnosis system based on the latest Deep-Learning (DL) models.The Gerry model, which use a Convolutional Neural Network (CNN) classification model, is being expanded to include four classes for lung disease.The proposed methodology demonstrates a substantial enhancement in accuracy, ranging from 0.432% to 1.621%, while concurrently reducing loss by 100% to 138%.CNN extends the procedure to incorporate a five-class model, which effectively differentiates between COVID-19, lung fibrosis, lung opacity, normal cases without anomalies, and pneumonia.We use a 22,851 Chest X-ray (CXR) image dataset to train, validate, and test the model.The resulting model has an impressive 92% overall accuracy.The following are the reported f1-scores, precision, and recall for each class: 91%, 89%, and 93% for lung opacity; 92%, 96%, and 93% for standard cases; 85%, 73%, and 78% for lung fibrosis; and 96%, 99%, and 97% for pneumonia.By this diagnostic method and with the aid of precise detection and categorization of various lung disorders, patient outcomes, and clinical decision-making can be potentially improved.