Presenting a Hybrid Deep Learning Model for Multi-Label Classification of Chest Diseases With the Aim of Improving the Detection of Rare Abnormalities

Background and Aims: Deep learning models have the ability to simultaneously diagnose multiple diseases in a single chest radiography image, which can greatly impact the speed and accuracy of diagnosis in the medical field. This study aimed to design a deep learning model for multi-disease classification of chest radiography images, enabling the recognition of multiple diseases at once. This model addresses six clinically significant classes which are: no findings, emphysema, pneumothorax, hernia, mass, and fibrosis. Methods: This research utilized the NIH ChestX-ray۸ dataset, which contains ۱۱۲,۱۲۰ images across ۱۴ different categories. A stratified distribution approach was adopted to avoid patient overlap and data leakage, resulting in the following divisions: training (۷۷,۰۰۰ images); validation (۱۶,۵۰۰ images); and test (۱۶,۵۰۰ images). Pre-processing data included adjusting contrast, color saturation, and normalizing brightness, while augmentation techniques such as rotation, reflection, scaling, translation, and shifting were applied. The proposed model was a mix of ConvNeXtBase for hierarchical feature extraction and VGG۱۶ for local pattern recognition. Learning with pretrained ImageNet weights and optimization with Adam were applied. A weighted cross-entropy loss function was used to mitigate imbalance between classes. Results: The hybrid ConvNeXtBase-VGG۱۶ model outperformed thirteen other architectures including DenseNet, ResNet, MobileNet, NASNet, and EfficientNet particularly in detecting rare conditions such as Hernia and Fibrosis. The final performance results were: accuracy of ۰.۹۳۱, weighted F۱-score of ۰.۹۰۷۳, recall of ۰.۹۳۱۰, precision of ۰.۸۹۲۳, and an AUC-PR of ۰.۲۴۸۶. Further analysis confirmed that the model is strong and reliable in detecting lung diseases based on sensitivity, specificity, and Youden index optimization. Conclusion: This study highlights how combining multiple models, utilizing transfer learning and managing data imbalance can significantly improve the accuracy and reliability of automated diagnostic systems for chest diseases.

کلیدواژه ها:

Chest Diseases ، ChestRadiography ، DeepLearning ، Multi-labelClassification ، TransferLearning