Deep Learning For Chest X-Ray Image Segmentation In Chronic Obstructive Pulmonary Disease Patients To Detect Pneumonitis

Abstract

This study investigates the impact of regional characteristics on deep learning segmentation performance, focusing on the rib in chest X-ray images. Utilizing 195 normal chest X-ray datasets and employing data augmentation with 5-fold cross-validation, ribs were segmented vertically and horizontally relative to anatomical landmarks. Results indicate a 6–7% segmentation performance variation based on rib region characteristics, underscoring the significance of considering anatomical landmarks in segmentation tasks. Furthermore, the study presents a comprehensive performance evaluation of proposed models, including the Deep R-CNN Model, LSTM, GRU, and ANN. The Deep R-CNN Model achieves exceptional accuracy of 98.4%, supported by notable Dice and Jaccard scores of 92.8% and 81.31%, respectively. LSTM demonstrates robust performance with an accuracy of 96.1% and comparable Dice and Jaccard scores, while GRU and ANN exhibit moderate to lower accuracy levels. These findings offer valuable insights into the effectiveness of different models for medical imaging applications, informing the development of more effective deep learning algorithms. Ultimately, this research contributes to advancing segmentation techniques in medical imaging, facilitating precise diagnosis and treatment planning, and enhancing overall healthcare outcomes. Continued research in this area holds promise for further improvements in deep learning segmentation methodologies and their application in clinical practice.