Leveraging DeepLabV3+ for Lung Region Delineation in Chest Radiographs using Convolutional Neural Network Approach

The advent of deep learning technologies has revolutionized the field of medical imaging, particularly through the application of sophisticated convolutional neural network (CNN) frameworks like DeepLabV3+ for lung region delineation in chest X-ray images. Given the critical importance of accurate lung segmentation for diagnosing pulmonary diseases, this paper introduces custom modifications to the DeepLabV3+ architecture to address the unique challenges of lung radiographs, such as variability in lung shapes and obscured boundaries due to pathological conditions. Our modified approach enhances segmentation accuracy and computational efficiency, crucial for effective diagnosis and treatment planning. Evaluations on a diverse dataset of chest radiographs demonstrate that our customized DeepLabV3+ model substantially outperforms the baseline and other state-of-the-art methods, achieving a Dice Coefficient (DSC) of 0.93 and an Intersection over Union (IoU) of 0.87. These results not only show significant improvements in capturing detailed lung features but also highlight the model’s potential in clinical settings for more precise patient diagnoses and care strategies. This study not only advances the application of deep learning in medical imaging but also sets a benchmark for future research in domain-specific challenges, illustrating the impact of tailored CNN modifications on medical diagnostic accuracy and performance.