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

被引:48
作者
Albahli, Saleh [1 ]
Rauf, Hafiz Tayyab [2 ]
Algosaibi, Abdulelah [3 ]
Balas, Valentina Emilia [4 ]
机构
[1] Qassim Univ, Coll Comp Sci, Dept Informat Technol, Buraydah, Saudi Arabia
[2] Staffordshire Univ, Ctr Smart Syst AI & Cybersecur, Stoke On Trent, Staffs, England
[3] King Faisal Univ, Dept Comp Sci, Al Hufuf, Saudi Arabia
[4] Aurel Vlaicu Univ Arad, Dept Automat & Appl Informat, Arad, Romania
关键词
Image classification; Chest diseases; InceptionResNetV2; Pathology;
D O I
10.7717/peerj-cs.495
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:1 / 17
页数:17
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