Noise-induced modality-specific pretext learning for pediatric chest X-ray image classification.
Front Artif Intell
; 7: 1419638, 2024.
Article
en En
| MEDLINE
| ID: mdl-39301479
ABSTRACT
Introduction:
Deep learning (DL) has significantly advanced medical image classification. However, it often relies on transfer learning (TL) from models pretrained on large, generic non-medical image datasets like ImageNet. Conversely, medical images possess unique visual characteristics that such general models may not adequately capture.Methods:
This study examines the effectiveness of modality-specific pretext learning strengthened by image denoising and deblurring in enhancing the classification of pediatric chest X-ray (CXR) images into those exhibiting no findings, i.e., normal lungs, or with cardiopulmonary disease manifestations. Specifically, we use a VGG-16-Sharp-U-Net architecture and leverage its encoder in conjunction with a classification head to distinguish normal from abnormal pediatric CXR findings. We benchmark this performance against the traditional TL approach, viz., the VGG-16 model pretrained only on ImageNet. Measures used for performance evaluation are balanced accuracy, sensitivity, specificity, F-score, Matthew's Correlation Coefficient (MCC), Kappa statistic, and Youden's index.Results:
Our findings reveal that models developed from CXR modality-specific pretext encoders substantially outperform the ImageNet-only pretrained model, viz., Baseline, and achieve significantly higher sensitivity (p < 0.05) with marked improvements in balanced accuracy, F-score, MCC, Kappa statistic, and Youden's index. A novel attention-based fuzzy ensemble of the pretext-learned models further improves performance across these metrics (Balanced accuracy 0.6376; Sensitivity 0.4991; F-score 0.5102; MCC 0.2783; Kappa 0.2782, and Youden's index0.2751), compared to Baseline (Balanced accuracy 0.5654; Sensitivity 0.1983; F-score 0.2977; MCC 0.1998; Kappa 0.1599, and Youden's index0.1327).Discussion:
The superior results of CXR modality-specific pretext learning and their ensemble underscore its potential as a viable alternative to conventional ImageNet pretraining for medical image classification. Results from this study promote further exploration of medical modality-specific TL techniques in the development of DL models for various medical imaging applications.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Front Artif Intell
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos
Pais de publicación:
Suiza