PGGAN-Based Anomaly Classification on Chest X-Ray Using Weighted Multi-Scale Similarity
To use artificial intelligence to assist in diagnoses applications, a model to utilize quality data is required, which results in massive time and cost. In medical data, data imbalance occurs because the amount of data with lesions is less than that without lesions. To overcome this limitation, this...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9508371/ |
| _version_ | 1829493010977521664 |
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| author | Hye-Jeong Kwon Dong-Hoon Shin Kyungyong Chung |
| author_facet | Hye-Jeong Kwon Dong-Hoon Shin Kyungyong Chung |
| author_sort | Hye-Jeong Kwon |
| collection | DOAJ |
| description | To use artificial intelligence to assist in diagnoses applications, a model to utilize quality data is required, which results in massive time and cost. In medical data, data imbalance occurs because the amount of data with lesions is less than that without lesions. To overcome this limitation, this study proposes a progressive growth of generative adversarial networks (PGGAN)-based anomaly classification on chest X-rays using weighted multi-scale similarity. An anomaly detection method is applied to learn the distribution of normal images to solve the problem of data imbalance. The use of PGGAN, which is a model that generates high-resolution images by gradually adding layers, enables to find image characteristics on a multi-scale and define the similarity between an original image and a generated image. The anomaly score is calculated by applying the weighted arithmetic mean to a resolution-by-resolution similarity. The threshold is defined after the analysis of the F1-score, and then the classification performance is evaluated. The accuracy of the proposed model was assessed using a confusion matrix and compared with that of a conventional classification model, and the efficiency was demonstrated through ablation studies. The classification accuracy of the test dataset was 0.8525. Compared to a U-net-based disease classifier with low-resolution which accuracy was 0.8410, the performance of the proposed model was 0.8507, exhibited an improvement. |
| first_indexed | 2024-12-16T06:26:14Z |
| format | Article |
| id | doaj.art-71e15a8b27284243a5259a4be7182f7b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T06:26:14Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-71e15a8b27284243a5259a4be7182f7b2022-12-21T22:41:00ZengIEEEIEEE Access2169-35362021-01-01911331511332510.1109/ACCESS.2021.31029549508371PGGAN-Based Anomaly Classification on Chest X-Ray Using Weighted Multi-Scale SimilarityHye-Jeong Kwon0https://orcid.org/0000-0002-6390-0765Dong-Hoon Shin1https://orcid.org/0000-0003-2616-0865Kyungyong Chung2https://orcid.org/0000-0002-6439-9992Department of Computer Science, Kyonggi University, Suwon-si, South KoreaDepartment of Computer Science, Kyonggi University, Suwon-si, South KoreaDivision of AI Computer Science and Engineering, Kyonggi University, Suwon-si, South KoreaTo use artificial intelligence to assist in diagnoses applications, a model to utilize quality data is required, which results in massive time and cost. In medical data, data imbalance occurs because the amount of data with lesions is less than that without lesions. To overcome this limitation, this study proposes a progressive growth of generative adversarial networks (PGGAN)-based anomaly classification on chest X-rays using weighted multi-scale similarity. An anomaly detection method is applied to learn the distribution of normal images to solve the problem of data imbalance. The use of PGGAN, which is a model that generates high-resolution images by gradually adding layers, enables to find image characteristics on a multi-scale and define the similarity between an original image and a generated image. The anomaly score is calculated by applying the weighted arithmetic mean to a resolution-by-resolution similarity. The threshold is defined after the analysis of the F1-score, and then the classification performance is evaluated. The accuracy of the proposed model was assessed using a confusion matrix and compared with that of a conventional classification model, and the efficiency was demonstrated through ablation studies. The classification accuracy of the test dataset was 0.8525. Compared to a U-net-based disease classifier with low-resolution which accuracy was 0.8410, the performance of the proposed model was 0.8507, exhibited an improvement.https://ieeexplore.ieee.org/document/9508371/Artificial intelligencecomputer-aided diagnosticsdeep learninghealthcarePGGANunsupervised learning |
| spellingShingle | Hye-Jeong Kwon Dong-Hoon Shin Kyungyong Chung PGGAN-Based Anomaly Classification on Chest X-Ray Using Weighted Multi-Scale Similarity IEEE Access Artificial intelligence computer-aided diagnostics deep learning healthcare PGGAN unsupervised learning |
| title | PGGAN-Based Anomaly Classification on Chest X-Ray Using Weighted Multi-Scale Similarity |
| title_full | PGGAN-Based Anomaly Classification on Chest X-Ray Using Weighted Multi-Scale Similarity |
| title_fullStr | PGGAN-Based Anomaly Classification on Chest X-Ray Using Weighted Multi-Scale Similarity |
| title_full_unstemmed | PGGAN-Based Anomaly Classification on Chest X-Ray Using Weighted Multi-Scale Similarity |
| title_short | PGGAN-Based Anomaly Classification on Chest X-Ray Using Weighted Multi-Scale Similarity |
| title_sort | pggan based anomaly classification on chest x ray using weighted multi scale similarity |
| topic | Artificial intelligence computer-aided diagnostics deep learning healthcare PGGAN unsupervised learning |
| url | https://ieeexplore.ieee.org/document/9508371/ |
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