SLSU Faculty Member Pioneers AI Solution for Enhanced Lung Disease Diagnosis

Southern Leyte State University (SLSU) commends Ms. Jannie Fleur V. Oraño of the College of Computer Studies and Information Technology for their innovative research, recently published in a Scopus-indexed journal. Their study, titled “A Convolutional Neural Network Classification System for Lung Diseases from Chest X-Rays,” showcases a cutting-edge AI-based diagnostic tool aimed at improving lung disease identification.

Lung diseases persist as a significant global health issue, affecting individuals across various age groups and lifestyles. In response, this research harnesses Convolutional Neural Networks (CNNs) to classify six key lung conditions—effusion, atelectasis, infiltration, nodule, mass, and pneumothorax—through chest X-ray analysis. The model, trained on 8,125 images, demonstrated strong performance with an accuracy of 82.53% and a Cohen’s kappa value of 0.788.

This CNN model, which can operate on both computer and mobile platforms, represents a significant step forward in providing supplementary diagnostic tools for radiologists, supporting them in the swift and accurate identification of lung diseases. As such, it aligns with Sustainable Development Goal 3 (Good Health and Well-being), focusing on harnessing technological advancements to enhance healthcare quality and accessibility.

SLSU celebrates the accomplishments of its faculty, recognizing Ms. Oraño’s dedication to research that not only brings technological innovation into the medical field but also addresses global health challenges. The university remains committed to fostering transformative studies that contribute meaningfully to both academic and societal progress.

How to cite: Lung Disease Classification Utilizing Convolutional Neural Network with CXR Imaging. (2023, December 4). IEEE Conference Publication | IEEE Xplore.

To read full content: https://ieeexplore.ieee.org/document/10435917