Systematic Framework for Pulmonary Disease Evaluation

Abstract: The necessity of automatically identifying anomalies in CT scans is highlighted by the rising incidence of lung ailments brought on by poor air quality. Through the analysis of large datasets, machine learning is essential for the effective detection of diseases like lung cancer through lung nodule assessment. Usually, there are three steps in the diagnostic process: pre-processing the images, using deep learning (DL) models, and deploying convolutional neural networks (CNNs). While DL algorithms extract essential information for precise classification, pre-processing improves the clarity of CT images. CNNs are especially good at seeing intricate patterns that enable accurate differentiation between tissues that are healthy and those that are not. The accuracy and efficiency of lung disease diagnostics are greatly increased by this integrated approach, which promotes quicker detection, better treatment planning, and better patient outcomes.