Multi-Label Transfer Learning for Identifying Lung Diseases using Chest X-Rays

Chest radiography presents one of the main medical imaging modalities for diagnosing lung diseases. To assist radiologists during interventional procedures, this paper aims at proposing a transfer learning-based classifier to automatically identify 14 different thoracic diseases in Chest X-ray (CXR)...

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Bibliographic Details
Published in2021 International Conference on Electronic Engineering (ICEEM) pp. 1 - 6
Main Authors El-Fiky, Azza, Shouman, Marwa Ahmed, Hamada, Salwa, El-Sayed, Ayman, Karar, Mohamed Esmail
Format Conference Proceeding
LanguageEnglish
Published IEEE 03.07.2021
Subjects
Biomedical imaging
classification
Computer-aided diagnosis
Deep learning
Diagnostic radiography
Pulmonary diseases
Task analysis
Thorax diseases
Transfer learning
X-ray
X-ray imaging
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Summary:Chest radiography presents one of the main medical imaging modalities for diagnosing lung diseases. To assist radiologists during interventional procedures, this paper aims at proposing a transfer learning-based classifier to automatically identify 14 different thoracic diseases in Chest X-ray (CXR) images. The proposed method is relied on deep residual neural networks with 50 layers (ResNet-50) to accomplish the diagnostic task of many chest diseases. In this study, a public dataset of 112,120 frontal radiograph images for Chest X-ray has been used for validating the proposed deep learning classifier. It achieved the best performance of multi-label classification of normal and 14 different lung diseases with an average area under curve (AUC) of 0.911 and F1-score of 0.66. This study demonstrated that the proposed ResNet-50 classifier as a transfer learning model outperforms other relevant methods in the previous studies for automatic multi-label classification of chest X-rays.
DOI:10.1109/ICEEM52022.2021.9480622