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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Bouabid Qabliyane
Sultan Moulay Slimane University
Computer Science & IT Engineering

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Pneumothorax detection using a learning focal point architecture Salah-Eddine Mansour; Bouabid Qabliyane; Abdelhak Sakhi; Zakaria Khoudi; Mohamed Baslam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2041-2052

Abstract

Automatic image segmentation and feature analysis play a crucial role in improving the accuracy and efficiency of disease diagnosis and treatment within modern medical practice. This study propose the use of the learning focal point (LFP) architecture, which is based on the LFP algorithm, to perform effective segmentation of medical images by dividing each image in the dataset into multiple meaningful zones. This zonal segmentation strategy enables the precise extraction of critical regions of interest that are most relevant for pathological analysis. The proposed approach is specifically applied to the detection of common pneumothorax in lung imaging, a condition that requires timely and accurate diagnosis. By concentrating on essential lung zones, the LFP architecture enhances the reliability and robustness of pneumothorax identification. The results demonstrate that this method has the potential to significantly assist clinicians by providing more accurate diagnostic support and facilitating earlier medical intervention, ultimately improving patient outcomes.
Co-Authors Abdelhak Sakhi Mohamed Baslam Salah-Eddine Mansour Zakaria Khoudi