CIMI: Classify and Itemize Medical Image System for PFT Big Data Based on Deep Learning
The value of pulmonary function test (PFT) data is increasing due to the advent of the Coronavirus Infectious Disease 19 (COVID-19) and increased respiratory disease. However, these PFT data cannot be directly used in clinical studies, because PFT results are stored in raw image files. In this study...
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MDPI AG
2020-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/23/8575 |
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| author | Tong Min Kim Seo-Joon Lee Hwa Young Lee Dong-Jin Chang Chang Ii Yoon In-Young Choi Kun-Ho Yoon |
| author_facet | Tong Min Kim Seo-Joon Lee Hwa Young Lee Dong-Jin Chang Chang Ii Yoon In-Young Choi Kun-Ho Yoon |
| author_sort | Tong Min Kim |
| collection | DOAJ |
| description | The value of pulmonary function test (PFT) data is increasing due to the advent of the Coronavirus Infectious Disease 19 (COVID-19) and increased respiratory disease. However, these PFT data cannot be directly used in clinical studies, because PFT results are stored in raw image files. In this study, the classification and itemization medical image (CIMI) system generates valuable data from raw PFT images by automatically classifying various PFT results, extracting texts, and storing them in the PFT database and Excel files. The deep-learning-based optical character recognition (OCR) technology was mainly used in CIMI to classify and itemize PFT images in St. Mary’s Hospital. CIMI classified seven types and itemized 913,059 texts from 14,720 PFT image sheets, which cannot be done by humans. The number, type, and location of texts that can be extracted by PFT type are all different, but CIMI solves this issue by classifying the PFT image sheets by type, allowing researchers to analyze the data. To demonstrate the superiority of CIMI, the validation results of CIMI were compared to the results of the other four algorithms. A total of 70 randomly selected sheets (ten sheets from each type) and 33,550 texts were used for the validation. The accuracy of CIMI was 95%, which was the highest accuracy among the other four algorithms. |
| first_indexed | 2024-03-10T14:26:01Z |
| format | Article |
| id | doaj.art-307645c903d34a55ad9c2b352124cd2e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T14:26:01Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-307645c903d34a55ad9c2b352124cd2e2023-11-20T22:57:32ZengMDPI AGApplied Sciences2076-34172020-11-011023857510.3390/app10238575CIMI: Classify and Itemize Medical Image System for PFT Big Data Based on Deep LearningTong Min Kim0Seo-Joon Lee1Hwa Young Lee2Dong-Jin Chang3Chang Ii Yoon4In-Young Choi5Kun-Ho Yoon6Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, KoreaDepartment of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul 06591, KoreaDivision of Pulmonology and Allergy, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, KoreaDepartment of Ophthalmology and Visual Science, The Catholic University of Korea College of Medicine, Seoul 06591, KoreaGraduate School of Medicine, The Catholic University of Korea, Seoul 06591, KoreaDepartment of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul 06591, KoreaDivision of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, KoreaThe value of pulmonary function test (PFT) data is increasing due to the advent of the Coronavirus Infectious Disease 19 (COVID-19) and increased respiratory disease. However, these PFT data cannot be directly used in clinical studies, because PFT results are stored in raw image files. In this study, the classification and itemization medical image (CIMI) system generates valuable data from raw PFT images by automatically classifying various PFT results, extracting texts, and storing them in the PFT database and Excel files. The deep-learning-based optical character recognition (OCR) technology was mainly used in CIMI to classify and itemize PFT images in St. Mary’s Hospital. CIMI classified seven types and itemized 913,059 texts from 14,720 PFT image sheets, which cannot be done by humans. The number, type, and location of texts that can be extracted by PFT type are all different, but CIMI solves this issue by classifying the PFT image sheets by type, allowing researchers to analyze the data. To demonstrate the superiority of CIMI, the validation results of CIMI were compared to the results of the other four algorithms. A total of 70 randomly selected sheets (ten sheets from each type) and 33,550 texts were used for the validation. The accuracy of CIMI was 95%, which was the highest accuracy among the other four algorithms.https://www.mdpi.com/2076-3417/10/23/8575artificial intelligencedeep learningbig datamedical imageimage processingoptical character recognition |
| spellingShingle | Tong Min Kim Seo-Joon Lee Hwa Young Lee Dong-Jin Chang Chang Ii Yoon In-Young Choi Kun-Ho Yoon CIMI: Classify and Itemize Medical Image System for PFT Big Data Based on Deep Learning Applied Sciences artificial intelligence deep learning big data medical image image processing optical character recognition |
| title | CIMI: Classify and Itemize Medical Image System for PFT Big Data Based on Deep Learning |
| title_full | CIMI: Classify and Itemize Medical Image System for PFT Big Data Based on Deep Learning |
| title_fullStr | CIMI: Classify and Itemize Medical Image System for PFT Big Data Based on Deep Learning |
| title_full_unstemmed | CIMI: Classify and Itemize Medical Image System for PFT Big Data Based on Deep Learning |
| title_short | CIMI: Classify and Itemize Medical Image System for PFT Big Data Based on Deep Learning |
| title_sort | cimi classify and itemize medical image system for pft big data based on deep learning |
| topic | artificial intelligence deep learning big data medical image image processing optical character recognition |
| url | https://www.mdpi.com/2076-3417/10/23/8575 |
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