AI-driven deep CNN approach for multi-label pathology classification using chest X-Rays

Saleh Albahli; Hafiz Tayyab Rauf; Abdulelah Algosaibi; Valentina Emilia Balas

doi:10.7717/peerj-cs.495

AI-driven deep CNN approach for multi-label pathology classification using chest X-Rays

PeerJ Comput Sci. 2021 Apr 20:7:e495. doi: 10.7717/peerj-cs.495. eCollection 2021.

Authors

Saleh Albahli¹, Hafiz Tayyab Rauf², Abdulelah Algosaibi³, Valentina Emilia Balas⁴

Affiliations

¹ Department of Information Technology, College of Computer Science, Qassim University, Buraydah, Saudi Arabia.
² Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, stoke on Trent, United Kingdom.
³ Department of Computer Science, King Faisal University, Hofuf, Saudi Arabia.
⁴ Department of Automation and Applied Informatics, Aurel Vlaicu University of Arad, Arad, Romania.

Abstract

Artificial intelligence (AI) has played a significant role in image analysis and feature extraction, applied to detect and diagnose a wide range of chest-related diseases. Although several researchers have used current state-of-the-art approaches and have produced impressive chest-related clinical outcomes, specific techniques may not contribute many advantages if one type of disease is detected without the rest being identified. Those who tried to identify multiple chest-related diseases were ineffective due to insufficient data and the available data not being balanced. This research provides a significant contribution to the healthcare industry and the research community by proposing a synthetic data augmentation in three deep Convolutional Neural Networks (CNNs) architectures for the detection of 14 chest-related diseases. The employed models are DenseNet121, InceptionResNetV2, and ResNet152V2; after training and validation, an average ROC-AUC score of 0.80 was obtained competitive as compared to the previous models that were trained for multi-class classification to detect anomalies in x-ray images. This research illustrates how the proposed model practices state-of-the-art deep neural networks to classify 14 chest-related diseases with better accuracy.

Keywords: Chest diseases; Image classification; InceptionResNetV2; Pathology.

Grants and funding

The Deanship of Scientific Research, Qassim University funded the publication of this project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.