Multi-Label Classification of Chest X-ray Abnormalities Using Transfer Learning Techniques

In recent years, deep neural networks have enabled countless innovations in the field of image classification. Encouraged by success in this field, researchers worldwide have demonstrated how to use Convolutional Neural Network techniques in medical imaging problems. In this article, the results wer...

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Bibliographic Details
Published in:Journal of personalized medicine 2023-09, Vol.13 (10), p.1426
Main Authors: Kufel, Jakub, Bielówka, Michał, Rojek, Marcin, Mitręga, Adam, Lewandowski, Piotr, Cebula, Maciej, Krawczyk, Dariusz, Bielówka, Marta, Kondoł, Dominika, Bargieł-Łączek, Katarzyna, Paszkiewicz, Iga, Czogalik, Łukasz, Kaczyńska, Dominika, Wocław, Aleksandra, Gruszczyńska, Katarzyna, Nawrat, Zbigniew
Format: Article
Language:English
Subjects:
Accuracy
Chest
Classification
Datasets
Deep learning
Hernias
Innovations
Medical imaging equipment
Methods
Neural networks
Pathology
Patients
Performance evaluation
Pneumonia
Precision medicine
Radiology
Regularization methods
Transfer learning
X-rays
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Summary:In recent years, deep neural networks have enabled countless innovations in the field of image classification. Encouraged by success in this field, researchers worldwide have demonstrated how to use Convolutional Neural Network techniques in medical imaging problems. In this article, the results were obtained through the use of the EfficientNet in the task of classifying 14 different diseases based on chest X-ray images coming from the NIH (National Institutes of Health) ChestX-ray14 dataset. The approach addresses dataset imbalances by introducing a custom split to ensure fair representation. Binary cross entropy loss is utilized to handle the multi-label difficulty. The model architecture comprises an EfficientNet backbone for feature extraction, succeeded by sequential layers including GlobalAveragePooling, Dense, and BatchNormalization. The main contribution of this paper is a proposed solution that outperforms previous state-of-the-art deep learning models average area under the receiver operating characteristic curve—AUC-ROC (score: 84.28%). The usage of the transfer-learning technique and traditional deep learning engineering techniques was shown to enable us to obtain such results on consumer-class GPUs (graphics processing units).
ISSN:2075-4426
2075-4426
DOI:10.3390/jpm13101426