Integrated deep learning-based IRACE and convolutional neural networks for chest X-ray image classification
When pre-trained models are applied directly to chest X-ray (CXR) images without appropriate adaptation, they frequently show problems like overfitting, limited generalization, or decreased SE to clinically relevant features because of the unique characteristics of medical data, such as class imbala...
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| Vydáno v: | Knowledge-based systems Ročník 329; s. 114293 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
04.11.2025
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| ISSN: | 0950-7051 |
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| Abstract | When pre-trained models are applied directly to chest X-ray (CXR) images without appropriate adaptation, they frequently show problems like overfitting, limited generalization, or decreased SE to clinically relevant features because of the unique characteristics of medical data, such as class imbalance and domain-specific noise. Due to the discrepancy between natural image features (used during pre-training) and radiological image characteristics, studies have shown that such models may perform well on training data but poorly on unseen clinical samples. This study comprehensively evaluates the performance of the fine-tuning method using the Iterated Race for Automatic Algorithm Configuration (IRACE) technique on pre-trained models for several medical imaging CXRs. We select five well-known CNN architectures: MobileNet-v2, EfficientNet-b0, ResNet-50, DenseNet-121, and VGG-19, utilizing the IRACE technique for HPT classification of three CXR datasets. The experimental results indicate that the IRACE technique was generally effective across CXR images, producing noticeable improvements on all models. DenseNet-121 outperformed the other architectures across all metrics, achieving accuracies of 99.83 %, 99.98 %, and 99.87 % on the three CXR datasets, respectively. Additionally, we explored the model detection mechanism by interpreting the classification of radiological images using the Gradient-weighted Class Activation Mapping (Grad-CAM) with Layer-wise Relevance Propagation (LRP) approach for CXR imaging. The results obtained have provided information on how the model classifies CXR images, which can assist radiologists in identifying and evaluating visual characteristics. |
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| AbstractList | When pre-trained models are applied directly to chest X-ray (CXR) images without appropriate adaptation, they frequently show problems like overfitting, limited generalization, or decreased SE to clinically relevant features because of the unique characteristics of medical data, such as class imbalance and domain-specific noise. Due to the discrepancy between natural image features (used during pre-training) and radiological image characteristics, studies have shown that such models may perform well on training data but poorly on unseen clinical samples. This study comprehensively evaluates the performance of the fine-tuning method using the Iterated Race for Automatic Algorithm Configuration (IRACE) technique on pre-trained models for several medical imaging CXRs. We select five well-known CNN architectures: MobileNet-v2, EfficientNet-b0, ResNet-50, DenseNet-121, and VGG-19, utilizing the IRACE technique for HPT classification of three CXR datasets. The experimental results indicate that the IRACE technique was generally effective across CXR images, producing noticeable improvements on all models. DenseNet-121 outperformed the other architectures across all metrics, achieving accuracies of 99.83 %, 99.98 %, and 99.87 % on the three CXR datasets, respectively. Additionally, we explored the model detection mechanism by interpreting the classification of radiological images using the Gradient-weighted Class Activation Mapping (Grad-CAM) with Layer-wise Relevance Propagation (LRP) approach for CXR imaging. The results obtained have provided information on how the model classifies CXR images, which can assist radiologists in identifying and evaluating visual characteristics. |
| ArticleNumber | 114293 |
| Author | Abdel Samee, Nagwan Saber, Eman Wang, Mingjing Hu, Gang Houssein, Essam H. |
| Author_xml | – sequence: 1 givenname: Nagwan orcidid: 0000-0001-5957-1383 surname: Abdel Samee fullname: Abdel Samee, Nagwan email: nmabdelsamee@pnu.edu.sa organization: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia – sequence: 2 givenname: Essam H. orcidid: 0000-0002-8127-7233 surname: Houssein fullname: Houssein, Essam H. email: essam.halim@mu.edu.eg organization: Faculty of Computers and Information, Minia University, Minia, Egypt – sequence: 3 givenname: Eman surname: Saber fullname: Saber, Eman email: eng.eman_saber@s-mu.edu.eg organization: Faculty of Computers and Information, Minia University, Minia, Egypt – sequence: 4 givenname: Gang surname: Hu fullname: Hu, Gang email: hugang@xaut.edu.cn organization: Department of Applied Mathematics, Xi’an University of Technology, Xi’an, 710054, China – sequence: 5 givenname: Mingjing surname: Wang fullname: Wang, Mingjing email: wangmingjing.style@gmail.com organization: School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, 325000, China |
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| Keywords | COVID-19 Iterated race for automatic algorithm configuration (IRACE) Pneumonia Convolutional neural networks (CNNs) Chest X-rays (CXR) Deep learning (DL) |
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