Browsing by Author "Alsurayhi, Halimah"

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Machine Learning Classififiers for Chronic Obstructive Pulmonary Disease Assessment Using Lung CT Data.
(Western University, 2024-04-12) Alsurayhi, Halimah; Abbas, Samani
Chronic Obstructive Pulmonary Disease (COPD) is a condition characterized by persistent inflammation and airflow blockages in the lungs, contributing to a significant number of deaths globally each year. To guide tailored treatment strategies and mitigate future risks, the Global Initiative for Chronic Obstructive Lung Disease (GOLD) employs a multifaceted assessment system of COPD severity, considering patient's lung function, symptoms, and exacerbation history. COPD staging systems, such as the high-resolution eight-stage COPD system and the GOLD 2023 three staging systems, have been later developed based on these factors. Lung Computed Tomography (CT) is becoming increasingly crucial in investigating COPD as it can detect various COPD phenotypes, such as emphysema, bronchial wall thickening, and gas trapping. Deep learning techniques show promise in leveraging CT imaging to assess the severity of COPD. This thesis uses lung CT data in conjunction with machine learning techniques to classify COPD patients according to these staging systems. For the eight-stage system, both Neural Network and Convolutional Neural Network (CNN) approaches were employed for classification. To develop the Neural Network model, features were extracted from lung CT scans at inspiration and expiration breathing phases, including lung air features and COPD phenotypes features. The CNN model utilized a single lung CT scan at the expiration phase. The GOLD 2023 three staging system involves training separate CNN models using lung CT scans at expiration to predict symptom levels and COPD exacerbation risk. In this thesis, in addition to models trained from scratch, Transfer Learning was also employed to develop models for the eight-stage COPD classification, Symptom level prediction, and exacerbation risk prediction. The developed classifiers demonstrate reasonably high classification performance, indicating their potential for deployment in clinical settings to enhance COPD assessment using image data.
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