Breast Mass Classification Using eLFA Algorithm Based on CRNN Deep Learning Model
Breast cancer is known to be common in many developed countries. It is reported as the most common type of cancer in the US, affecting one in eight women. In Korea, thyroid cancer is the most common type of cancer, followed by breast cancer in women. Considering this, early detection and accurate di...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9245496/ |
| _version_ | 1819132700545515520 |
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| author | Chang-Min Kim Roy C. Park Ellen J. Hong |
| author_facet | Chang-Min Kim Roy C. Park Ellen J. Hong |
| author_sort | Chang-Min Kim |
| collection | DOAJ |
| description | Breast cancer is known to be common in many developed countries. It is reported as the most common type of cancer in the US, affecting one in eight women. In Korea, thyroid cancer is the most common type of cancer, followed by breast cancer in women. Considering this, early detection and accurate diagnosis of breast cancer are crucial for reducing the associated death rate. Recently, cancer diagnosis systems using medical images have attracted significant attention. Medical imaging methods, such as computed tomography and magnetic resonance imaging, can reveal the overall shape, heterogeneity, and growth speed of carcinoma and are, thus, more commonly employed for diagnoses. Medical imaging has gained popularity since a recent study identified that it could reflect the gene phenotype of a patient. However, an aided diagnosis system based on medical images requires high-specification equipment to analyze high-resolution data. Therefore, this article proposes an edge extraction algorithm and a modified convolutional recurrent neural network (CRNN) model to accurately assess breast cancer based on medical imaging. The proposed algorithm extracts line-segment information from a breast mass image. The extracted line segments were classified into 16 types. Each type was uniquely labeled and compressed. The image compressed in this process was used as the input for the modified CRNN model. Traditional deep learning models were used to evaluate the performance of the proposed algorithm. The results show that the proposed model had the highest accuracy and lowest loss (99.75% and 0.0257, respectively). |
| first_indexed | 2024-12-22T09:35:34Z |
| format | Article |
| id | doaj.art-5f357cf290d54fd087507cb5b2d1244d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T09:35:34Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5f357cf290d54fd087507cb5b2d1244d2022-12-21T18:30:50ZengIEEEIEEE Access2169-35362020-01-01819731219732310.1109/ACCESS.2020.30349149245496Breast Mass Classification Using eLFA Algorithm Based on CRNN Deep Learning ModelChang-Min Kim0https://orcid.org/0000-0002-9735-6921Roy C. Park1https://orcid.org/0000-0002-4348-9753Ellen J. Hong2https://orcid.org/0000-0002-0948-9944Division of Computer Information Engineering, Sangji University, Wonju, South KoreaDepartment of Information Communication Software Engineering, Sangji University, Wonju, South KoreaDepartment of Computer and Telecommunications Engineering, Yonsei University, Wonju, South KoreaBreast cancer is known to be common in many developed countries. It is reported as the most common type of cancer in the US, affecting one in eight women. In Korea, thyroid cancer is the most common type of cancer, followed by breast cancer in women. Considering this, early detection and accurate diagnosis of breast cancer are crucial for reducing the associated death rate. Recently, cancer diagnosis systems using medical images have attracted significant attention. Medical imaging methods, such as computed tomography and magnetic resonance imaging, can reveal the overall shape, heterogeneity, and growth speed of carcinoma and are, thus, more commonly employed for diagnoses. Medical imaging has gained popularity since a recent study identified that it could reflect the gene phenotype of a patient. However, an aided diagnosis system based on medical images requires high-specification equipment to analyze high-resolution data. Therefore, this article proposes an edge extraction algorithm and a modified convolutional recurrent neural network (CRNN) model to accurately assess breast cancer based on medical imaging. The proposed algorithm extracts line-segment information from a breast mass image. The extracted line segments were classified into 16 types. Each type was uniquely labeled and compressed. The image compressed in this process was used as the input for the modified CRNN model. Traditional deep learning models were used to evaluate the performance of the proposed algorithm. The results show that the proposed model had the highest accuracy and lowest loss (99.75% and 0.0257, respectively).https://ieeexplore.ieee.org/document/9245496/Computer aided diagnosis systemline feature analysisCRNNbreast cancerdeep learning |
| spellingShingle | Chang-Min Kim Roy C. Park Ellen J. Hong Breast Mass Classification Using eLFA Algorithm Based on CRNN Deep Learning Model IEEE Access Computer aided diagnosis system line feature analysis CRNN breast cancer deep learning |
| title | Breast Mass Classification Using eLFA Algorithm Based on CRNN Deep Learning Model |
| title_full | Breast Mass Classification Using eLFA Algorithm Based on CRNN Deep Learning Model |
| title_fullStr | Breast Mass Classification Using eLFA Algorithm Based on CRNN Deep Learning Model |
| title_full_unstemmed | Breast Mass Classification Using eLFA Algorithm Based on CRNN Deep Learning Model |
| title_short | Breast Mass Classification Using eLFA Algorithm Based on CRNN Deep Learning Model |
| title_sort | breast mass classification using elfa algorithm based on crnn deep learning model |
| topic | Computer aided diagnosis system line feature analysis CRNN breast cancer deep learning |
| url | https://ieeexplore.ieee.org/document/9245496/ |
| work_keys_str_mv | AT changminkim breastmassclassificationusingelfaalgorithmbasedoncrnndeeplearningmodel AT roycpark breastmassclassificationusingelfaalgorithmbasedoncrnndeeplearningmodel AT ellenjhong breastmassclassificationusingelfaalgorithmbasedoncrnndeeplearningmodel |