WCSE

Evaluating Performance of Multiclass Chest Radiograph Classification Using Soft Voting of CNNs and Transformers

Abstract— Accurate and sensitive classification of multiple thoracic diseases using chest radiographs remains a significant challenge in computer-aided diagnosis. While Convolutional Neural Networks (CNNs) and Transformer-based models have shown strong performance individually is often limited. This study proposes a deep learning framework to improve the area under the curve (AUC) for multiclass detection of 14 chest diseases. We utilized the NIH ChestX-ray14 dataset, which includes 112,120 frontal-view chest radiographs from 30,805 unique patients, annotated with 14 disease labels. Predictions from five models—including a CNN model (DenseNet121), two Transfor mer models (Vision Transformer and Swin Transformer), and two hybrid models (Hybrid ViT–DenseNet121 and Hybrid Swin–DenseNet121)—were integrated using soft voting to enhance classification robustness. Soft voting achieved an AUC of 0.8437, significantly higher than all individual or hybrid models (all P < 0.05), except DenseNet121 (AUC = 0.8433; P = 0.76). This framework significantly improves AUC for multiclass chest disease detection and offers a robust approach for more reliable and sensitive clinical screening applications.

Index Terms— Chest radiograph, convolutional neural network, transformer

Hui-Chu Chiu
Ph.D. Program of Management, Chung-Hua University, Taiwan
Deng-Yiu Chiu
Department of Information Management, Chung-Hua University, Taiwan.
Usama Yasir Khan, Chia-Ching Chang, Chun-Jung Juan
Department of Medical Imaging, China Medical University Hsinchu Hospital,Taiwan
Chia-Ching Chang
Department of Management Science, National Yang Ming Chiao Tung University, Taiwan
Cheng-Hsuan Juan
Department of Obstetrics and Gynecology, Cheng Ching Hospital Chung Kang Branch,Taiwan
Yao-Hsien Lee
Department of Finance, Chung Hua University, Taiwan
Chen-Shu Wang
Department of Information and Finance Management,Taiwan
Chun-Jung Juan
Department of Medical Imaging, Medical University Hospital, Taiwan; Department of Radiology,School of Medicine, China Medical University, Taiwan; Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University,Taiwan; Department of Computer Science and Information Engineering, National Taiwan University, Taiwan

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Cite: Hui-Chu Chiu, Deng-Yiu Chiu, Usama Yasir Khan, Chia-Ching Chang, Cheng-Hsuan Juan, Yao-Hsien Lee, Chen-Shu Wang, Chun-Jung Juan, "Evaluating Performance of Multiclass Chest Radiograph Classification Using Soft Voting of CNNs and Transformers", 2025 the 15th International Workshop on Computer Science and Engineering (WCSE 2025), pp. 201-205, Jeju Island, South Korea, June 28-30, 2025.