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Optical Based Gradient-Weighted Class Activation Mapping and Transfer Learning Integrated Pneumonia Prediction Model

Chia-Wei Jan1, Yu-Jhih Chiu1, Kuan-Lin Chen2, Ting-Chun Yao3, Ping-Huan Kuo1,4,*

1 Department of Mechanical Engineering, National Chung Cheng University, Chiayi, 62102, Taiwan
2 Department of Intelligent Robotics, National Pingtung University, Pingtung, 900392, Taiwan
3 School of Medicine, College of Medicine, Taipei Medical University, Taipei City, Taiwan
4 Advanced Institute of Manufacturing with High-Tech Innovations (AIM-HI), National Chung Cheng University, Chiayi, 62102, Taiwan

* Corresponding Author: Ping-Huan Kuo. Email: email

(This article belongs to the Special Issue: Artificial Intelligence for clinical medicine and computer-aided diagnosis (CAD))

Computer Systems Science and Engineering 2023, 47(3), 2989-3010. https://doi.org/10.32604/csse.2023.042078

Abstract

Pneumonia is a common lung disease that is more prone to affect the elderly and those with weaker respiratory systems. However, hospital medical resources are limited, and sometimes the workload of physicians is too high, which can affect their judgment. Therefore, a good medical assistance system is of great significance for improving the quality of medical care. This study proposed an integrated system by combining transfer learning and gradient-weighted class activation mapping (Grad-CAM). Pneumonia is a common lung disease that is generally diagnosed using X-rays. However, in areas with limited medical resources, a shortage of medical personnel may result in delayed diagnosis and treatment during the critical period. Additionally, overworked physicians may make diagnostic errors. Therefore, having an X-ray pneumonia diagnosis assistance system is a significant tool for improving the quality of medical care. The result indicates that the best results were obtained by a ResNet50 pretrained model combined with a fully connected classification layer. A retraining procedure was designed to improve accuracy by using gradient-weighted class activation mapping (Grad-CAM), which detects the misclassified images and adds weights to them. In the evaluation tests, the final combined model is named Grad-CAM Based Pneumonia Network (GCPNet) out performed its counterparts in terms of accuracy, precision, and F1 score and reached 97.2% accuracy. An integrated system is proposed to increase model performance where Grad-CAM and transfer learning are combined. Grad-CAM is used to generate the heatmap, which shows the region that the model is focusing on. The outcomes of this research can aid in diagnosing pneumonia symptoms, as the model can accurately classify chest X-ray images, and the heatmap can assist doctors in observing the crucial areas.

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APA Style
Jan, C., Chiu, Y., Chen, K., Yao, T., Kuo, P. (2023). Optical based gradient-weighted class activation mapping and transfer learning integrated pneumonia prediction model. Computer Systems Science and Engineering, 47(3), 2989-3010. https://doi.org/10.32604/csse.2023.042078
Vancouver Style
Jan C, Chiu Y, Chen K, Yao T, Kuo P. Optical based gradient-weighted class activation mapping and transfer learning integrated pneumonia prediction model. Comput Syst Sci Eng. 2023;47(3):2989-3010 https://doi.org/10.32604/csse.2023.042078
IEEE Style
C. Jan, Y. Chiu, K. Chen, T. Yao, and P. Kuo "Optical Based Gradient-Weighted Class Activation Mapping and Transfer Learning Integrated Pneumonia Prediction Model," Comput. Syst. Sci. Eng., vol. 47, no. 3, pp. 2989-3010. 2023. https://doi.org/10.32604/csse.2023.042078



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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