Revolutionizing Lung Segmentation with Machine Learning: A Critical Review of Techniques in Medical Imaging - Momina Aisha; Moazma Ijaz; Nimra Tariq; Sehar Anjum; Sidra Siddiqui; Usman Hashmi (Sir Syed University Research Journal of Engineering and Technology)
Medical imaging is a critical tool for diagnosing and treating various diseases such as Chronic Obstructive Pulmonary Disease (COPD), tuberculosis, lung cancer, and Coronavirus. Techniques such as X-rays, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET) play essential roles in identifying the physical and functional aspects of the lungs. Manual lung segmentation by radiologists, while adjustable, is time-consuming and subject to variability. Consequently, automated lung segmentation methods utilizing Machine Learning (ML) and Deep Learning (DL) have emerged as essential alternatives. This review highlights advancements in automated lung segmentation, focusing on traditional ML methods and state-of-the-art DL approaches, particularly Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). While these techniques hold great promise, challenges remain, such as the need for annotated datasets, computational demands, and integration into clinical workflows. This paper explores current applications, identifies challenges, and outlines future opportunities for improving the precision and efficiency of lung segmentation through interdisciplinary collaboration in medical imaging, computer science, and clinical practice.