A New Approach to Feature Extraction Based on Lung CT Images Using Machine Learning Algorithms for Lung Disease Classification

Accurate diagnosis of lung diseases based on processing and analyzing lung CT images is crucial for aiding medical decision-making. This study presents a new feature extraction method based on human tissue density patterns, called Analysis of Human Tissue Density (AHTD). This method is compared with the Gray Level Co-occurrence Matrix (GLCM), Hu Moments (HM), Statistical Moments (SM), and Zernike Moments (ZM). The dataset of chest tomography images was obtained from the Walter Cantidio University Hospital in Fortaleza, Brazil. Four machine learning classifiers were used in this study: Bayesian Classifier, Optimum-Path Forest (OPF), k-Nearest Neighbors (KNN), and Support Vector Machine (SVM) to classify lung diseases in chest images. Feature extraction from lung images was performed in ۵.۲ milliseconds, achieving an accuracy of ۹۹.۰۱% for lung disease diagnosis and classification. The results of this study suggest that the proposed method can be used in real-time applications due to its rapid processing time and high accuracy for classifying lung diseases based on lung CT images.

Keywords:

Human Tissue Density Analysis , Gray Level Co-occurrence Matrix , Lung Disease , Moments , Machine Learning , Feature Extraction , Support Vector Machine , Optimum-Path Forest