Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different Times
Medical professionals in thoracic medicine routinely analyze chest X-ray images, often comparing pairs of images taken at different times to detect lesions or anomalies in patients. This research aims to design a computer-aided diagnosis system that enhances the efficiency of thoracic physicians in...
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MDPI AG
2024-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/5/1478 |
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| author | Sung-Nien Yu Meng-Chin Chiu Yu Ping Chang Chi-Yen Liang Wei Chen |
| author_facet | Sung-Nien Yu Meng-Chin Chiu Yu Ping Chang Chi-Yen Liang Wei Chen |
| author_sort | Sung-Nien Yu |
| collection | DOAJ |
| description | Medical professionals in thoracic medicine routinely analyze chest X-ray images, often comparing pairs of images taken at different times to detect lesions or anomalies in patients. This research aims to design a computer-aided diagnosis system that enhances the efficiency of thoracic physicians in comparing and diagnosing X-ray images, ultimately reducing misjudgments. The proposed system encompasses four key components: segmentation, alignment, comparison, and classification of lung X-ray images. Utilizing a public NIH Chest X-ray14 dataset and a local dataset gathered by the Chiayi Christian Hospital in Taiwan, the efficacy of both the traditional methods and deep-learning methods were compared. Experimental results indicate that, in both the segmentation and alignment stages, the deep-learning method outperforms the traditional method, achieving higher average IoU, detection rates, and significantly reduced processing time. In the comparison stage, we designed nonlinear transfer functions to highlight the differences between pre- and post-images through heat maps. In the classification stage, single-input and dual-input network architectures were proposed. The inclusion of difference information in single-input networks enhances AUC by approximately 1%, and dual-input networks achieve a 1.2–1.4% AUC increase, underscoring the importance of difference images in lung disease identification and classification based on chest X-ray images. While the proposed system is still in its early stages and far from clinical application, the results demonstrate potential steps forward in the development of a comprehensive computer-aided diagnostic system for comparative analysis of chest X-ray images. |
| first_indexed | 2024-04-25T00:20:01Z |
| format | Article |
| id | doaj.art-5c571c8feb9c4e3c987f1c6f101660f6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-25T00:20:01Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5c571c8feb9c4e3c987f1c6f101660f62024-03-12T16:54:54ZengMDPI AGSensors1424-82202024-02-01245147810.3390/s24051478Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different TimesSung-Nien Yu0Meng-Chin Chiu1Yu Ping Chang2Chi-Yen Liang3Wei Chen4Department of Electrical Engineering, National Chung Cheng University, Chiayi County 621301, TaiwanDepartment of Electrical Engineering, National Chung Cheng University, Chiayi County 621301, TaiwanDepartment of Electrical Engineering, National Chung Cheng University, Chiayi County 621301, TaiwanDivision of Pulmonary and Critical Care Medicine, Chiayi Christian Hospital, Chiayi County 600566, TaiwanDivision of Pulmonary and Critical Care Medicine, Chiayi Christian Hospital, Chiayi County 600566, TaiwanMedical professionals in thoracic medicine routinely analyze chest X-ray images, often comparing pairs of images taken at different times to detect lesions or anomalies in patients. This research aims to design a computer-aided diagnosis system that enhances the efficiency of thoracic physicians in comparing and diagnosing X-ray images, ultimately reducing misjudgments. The proposed system encompasses four key components: segmentation, alignment, comparison, and classification of lung X-ray images. Utilizing a public NIH Chest X-ray14 dataset and a local dataset gathered by the Chiayi Christian Hospital in Taiwan, the efficacy of both the traditional methods and deep-learning methods were compared. Experimental results indicate that, in both the segmentation and alignment stages, the deep-learning method outperforms the traditional method, achieving higher average IoU, detection rates, and significantly reduced processing time. In the comparison stage, we designed nonlinear transfer functions to highlight the differences between pre- and post-images through heat maps. In the classification stage, single-input and dual-input network architectures were proposed. The inclusion of difference information in single-input networks enhances AUC by approximately 1%, and dual-input networks achieve a 1.2–1.4% AUC increase, underscoring the importance of difference images in lung disease identification and classification based on chest X-ray images. While the proposed system is still in its early stages and far from clinical application, the results demonstrate potential steps forward in the development of a comprehensive computer-aided diagnostic system for comparative analysis of chest X-ray images.https://www.mdpi.com/1424-8220/24/5/1478lung diseasechest X-ray imageimage segmentationimage alignmentdeep learning |
| spellingShingle | Sung-Nien Yu Meng-Chin Chiu Yu Ping Chang Chi-Yen Liang Wei Chen Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different Times Sensors lung disease chest X-ray image image segmentation image alignment deep learning |
| title | Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different Times |
| title_full | Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different Times |
| title_fullStr | Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different Times |
| title_full_unstemmed | Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different Times |
| title_short | Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different Times |
| title_sort | improving computer aided thoracic disease diagnosis through comparative analysis using chest x ray images taken at different times |
| topic | lung disease chest X-ray image image segmentation image alignment deep learning |
| url | https://www.mdpi.com/1424-8220/24/5/1478 |
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