Leveraging LFP Architecture for Pneumothorax Detection in Chest X-rays - Mansour, Salah-Eddine; Sakhi, Abdelhak (Journal of Robotics and Control (JRC), 2025)
The frequency of pneumothorax diagnoses has risen since the COVID-19 pandemic, leading to an increase in related research. This study presents a novel approach for pneumothorax detection using the Learning Focal Point (LFP) architecture, which is based on the LFP algorithm. The LFP architecture segments chest X-ray images into multiple zones, allowing for the effective extraction of critical regions associated with pneumothorax. By focusing on these essential zones, the method aims to enhance the accuracy and reliability of detection, optimizing both training and testing processes. Unlike traditional methods that process the entire image, the LFP architecture prioritizes the most relevant areas, improving the efficiency of the model. Our results demonstrate a significant improvement in detection accuracy, achieving an impressive score of 0.87. This advancement holds promise for aiding clinicians in making more accurate diagnoses and providing timely interventions for patients suffering from pneumothorax. The proposed LFP-based method can be a valuable tool in medical imaging, particularly in the context of emergency care, where rapid and reliable diagnosis is crucial. Overall, the study highlights the potential of the LFP architecture to improve pneumothorax detection and contribute to the advancement of medical diagnostic technologies.