Health Risk Detection and Classification Model Using Multi-Model-Based Image Channel Expansion and Visual Pattern Standardization
Although mammography is an effective screening method for early detection of breast cancer, it is also difficult for experts to use since it requires a high level of sensitivity and expertise. A computer-aided detection system was introduced to improve the detection accuracy of breast cancer in mamm...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/18/8621 |
| _version_ | 1797520339827163136 |
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| author | Chang-Min Kim Ellen J. Hong Kyungyong Chung Roy C. Park |
| author_facet | Chang-Min Kim Ellen J. Hong Kyungyong Chung Roy C. Park |
| author_sort | Chang-Min Kim |
| collection | DOAJ |
| description | Although mammography is an effective screening method for early detection of breast cancer, it is also difficult for experts to use since it requires a high level of sensitivity and expertise. A computer-aided detection system was introduced to improve the detection accuracy of breast cancer in mammography, which is difficult to read. In addition, research to find lesions in mammography images using artificial intelligence has been actively conducted in recent days. However, the images generally used for breast cancer diagnosis are high-resolution and thus require high-spec equipment and a significant amount of time and money to learn and recognize the images and process calculations. This can lower the accuracy of the diagnosis since it depends on the performance of the equipment. To solve this problem, this paper will propose a health risk detection and classification model using multi-model-based image channel expansion and visual pattern shaping. The proposed method expands the channels of breast ultrasound images and detects tumors quickly and accurately through the YOLO model. In order to reduce the amount of computation to enable rapid diagnosis of the detected tumors, the model reduces the dimensions of the data by normalizing the visual information and use them as an input for the RNN model to diagnose breast cancer. When the channels were expanded through the proposed brightness smoothing and visual pattern shaping, the accuracy was the highest at 94.9%. Based on the images generated, the study evaluated the breast cancer diagnosis performance. The results showed that the accuracy of the proposed model was 97.3%, CRNN 95.2%, VGG 93.6%, AlexNet 62.9%, and GoogleNet 75.3%, confirming that the proposed model had the best performance. |
| first_indexed | 2024-03-10T07:55:22Z |
| format | Article |
| id | doaj.art-a8310e0407fe418b937422f0a5ed257f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T07:55:22Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a8310e0407fe418b937422f0a5ed257f2023-11-22T11:55:38ZengMDPI AGApplied Sciences2076-34172021-09-011118862110.3390/app11188621Health Risk Detection and Classification Model Using Multi-Model-Based Image Channel Expansion and Visual Pattern StandardizationChang-Min Kim0Ellen J. Hong1Kyungyong Chung2Roy C. Park3Division of Computer Information Engineering, Sangji University, Wonju 26339, KoreaDivision of Software, Yonsei University, Wonju 26493, KoreaDivision of AI Computer Science and Engineering, Kyonggi University, Suwon 16227, KoreaDepartment of Information Communication Software Engineering, Sangji University, Wonju 26339, KoreaAlthough mammography is an effective screening method for early detection of breast cancer, it is also difficult for experts to use since it requires a high level of sensitivity and expertise. A computer-aided detection system was introduced to improve the detection accuracy of breast cancer in mammography, which is difficult to read. In addition, research to find lesions in mammography images using artificial intelligence has been actively conducted in recent days. However, the images generally used for breast cancer diagnosis are high-resolution and thus require high-spec equipment and a significant amount of time and money to learn and recognize the images and process calculations. This can lower the accuracy of the diagnosis since it depends on the performance of the equipment. To solve this problem, this paper will propose a health risk detection and classification model using multi-model-based image channel expansion and visual pattern shaping. The proposed method expands the channels of breast ultrasound images and detects tumors quickly and accurately through the YOLO model. In order to reduce the amount of computation to enable rapid diagnosis of the detected tumors, the model reduces the dimensions of the data by normalizing the visual information and use them as an input for the RNN model to diagnose breast cancer. When the channels were expanded through the proposed brightness smoothing and visual pattern shaping, the accuracy was the highest at 94.9%. Based on the images generated, the study evaluated the breast cancer diagnosis performance. The results showed that the accuracy of the proposed model was 97.3%, CRNN 95.2%, VGG 93.6%, AlexNet 62.9%, and GoogleNet 75.3%, confirming that the proposed model had the best performance.https://www.mdpi.com/2076-3417/11/18/8621YOLOimage channel expansionbreast cancervisual pattern standardizationLFA |
| spellingShingle | Chang-Min Kim Ellen J. Hong Kyungyong Chung Roy C. Park Health Risk Detection and Classification Model Using Multi-Model-Based Image Channel Expansion and Visual Pattern Standardization Applied Sciences YOLO image channel expansion breast cancer visual pattern standardization LFA |
| title | Health Risk Detection and Classification Model Using Multi-Model-Based Image Channel Expansion and Visual Pattern Standardization |
| title_full | Health Risk Detection and Classification Model Using Multi-Model-Based Image Channel Expansion and Visual Pattern Standardization |
| title_fullStr | Health Risk Detection and Classification Model Using Multi-Model-Based Image Channel Expansion and Visual Pattern Standardization |
| title_full_unstemmed | Health Risk Detection and Classification Model Using Multi-Model-Based Image Channel Expansion and Visual Pattern Standardization |
| title_short | Health Risk Detection and Classification Model Using Multi-Model-Based Image Channel Expansion and Visual Pattern Standardization |
| title_sort | health risk detection and classification model using multi model based image channel expansion and visual pattern standardization |
| topic | YOLO image channel expansion breast cancer visual pattern standardization LFA |
| url | https://www.mdpi.com/2076-3417/11/18/8621 |
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