Hybrid Intelligent Pattern Recognition Systems for Mass Segmentation and Classification: A Pilot Study on Full-Field Digital Mammograms
Governments and health authorities emphasize the importance of early detection of breast cancer, usually through mammography, to improve prognosis, increase therapeutic options and achieve optimum outcomes. Despite technological advances and the advent of full-field digital mammography (FFDM), diagn...
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| Format: | Article |
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MDPI AG
2023-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/18/10401 |
| _version_ | 1797581385344483328 |
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| author | Anastasios Dounis Andreas-Nestor Avramopoulos Maria Kallergi |
| author_facet | Anastasios Dounis Andreas-Nestor Avramopoulos Maria Kallergi |
| author_sort | Anastasios Dounis |
| collection | DOAJ |
| description | Governments and health authorities emphasize the importance of early detection of breast cancer, usually through mammography, to improve prognosis, increase therapeutic options and achieve optimum outcomes. Despite technological advances and the advent of full-field digital mammography (FFDM), diagnosis of breast abnormalities on mammographic images remains a challenge due to qualitative variations in different tissue types and densities. Highly accurate computer-aided diagnosis (CADx) systems could assist in the differentiation between normal and abnormal tissue and the classification of abnormal tissue as benign or malignant. In this paper, classical, advanced fuzzy sets and fusion techniques for image enhancement were combined with three different thresholding methods (Global, Otsu and type-2 fuzzy sets threshold) and three different classifying techniques (K-means, FCM and ANFIS) for the classification of breast masses on FFDM. The aim of this paper is to identify the performance of the advanced fuzzy sets, fuzzy sets type-2 segmentation, decisions based on K-means and FCM, and the ANFIS classifier. Sixty-three combinations were evaluated on ninety-seven digital mammographic masses (sixty-five benign and thirty-two malignant). The performance of the sixty-three combinations was evaluated by estimating the accuracy, the F1 score, and the area under the curve (AUC). LH-XWW enhancement method with Otsu thresholding and FCM classifier outperformed all other combinations with an accuracy of 95.17%, F1 score of 89.42% and AUC of 0.91. This algorithm seems to offer a promising CADx system for breast cancer diagnosis on FFDM. |
| first_indexed | 2024-03-10T23:04:30Z |
| format | Article |
| id | doaj.art-94dc97f971fc4c6688225b7d9a112d7d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:04:30Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-94dc97f971fc4c6688225b7d9a112d7d2023-11-19T09:27:14ZengMDPI AGApplied Sciences2076-34172023-09-0113181040110.3390/app131810401Hybrid Intelligent Pattern Recognition Systems for Mass Segmentation and Classification: A Pilot Study on Full-Field Digital MammogramsAnastasios Dounis0Andreas-Nestor Avramopoulos1Maria Kallergi2Department of Biomedical Engineering, Egaleo Park Campus, University of West Attica, 12243 Athens, GreeceDepartment of Biomedical Engineering, Egaleo Park Campus, University of West Attica, 12243 Athens, GreeceDepartment of Biomedical Engineering, Egaleo Park Campus, University of West Attica, 12243 Athens, GreeceGovernments and health authorities emphasize the importance of early detection of breast cancer, usually through mammography, to improve prognosis, increase therapeutic options and achieve optimum outcomes. Despite technological advances and the advent of full-field digital mammography (FFDM), diagnosis of breast abnormalities on mammographic images remains a challenge due to qualitative variations in different tissue types and densities. Highly accurate computer-aided diagnosis (CADx) systems could assist in the differentiation between normal and abnormal tissue and the classification of abnormal tissue as benign or malignant. In this paper, classical, advanced fuzzy sets and fusion techniques for image enhancement were combined with three different thresholding methods (Global, Otsu and type-2 fuzzy sets threshold) and three different classifying techniques (K-means, FCM and ANFIS) for the classification of breast masses on FFDM. The aim of this paper is to identify the performance of the advanced fuzzy sets, fuzzy sets type-2 segmentation, decisions based on K-means and FCM, and the ANFIS classifier. Sixty-three combinations were evaluated on ninety-seven digital mammographic masses (sixty-five benign and thirty-two malignant). The performance of the sixty-three combinations was evaluated by estimating the accuracy, the F1 score, and the area under the curve (AUC). LH-XWW enhancement method with Otsu thresholding and FCM classifier outperformed all other combinations with an accuracy of 95.17%, F1 score of 89.42% and AUC of 0.91. This algorithm seems to offer a promising CADx system for breast cancer diagnosis on FFDM.https://www.mdpi.com/2076-3417/13/18/10401hybrid intelligent systemFuzzy C-meansadaptive neuro-fuzzy inference systemK-meanstype-2 fuzzy setsimage segmentation |
| spellingShingle | Anastasios Dounis Andreas-Nestor Avramopoulos Maria Kallergi Hybrid Intelligent Pattern Recognition Systems for Mass Segmentation and Classification: A Pilot Study on Full-Field Digital Mammograms Applied Sciences hybrid intelligent system Fuzzy C-means adaptive neuro-fuzzy inference system K-means type-2 fuzzy sets image segmentation |
| title | Hybrid Intelligent Pattern Recognition Systems for Mass Segmentation and Classification: A Pilot Study on Full-Field Digital Mammograms |
| title_full | Hybrid Intelligent Pattern Recognition Systems for Mass Segmentation and Classification: A Pilot Study on Full-Field Digital Mammograms |
| title_fullStr | Hybrid Intelligent Pattern Recognition Systems for Mass Segmentation and Classification: A Pilot Study on Full-Field Digital Mammograms |
| title_full_unstemmed | Hybrid Intelligent Pattern Recognition Systems for Mass Segmentation and Classification: A Pilot Study on Full-Field Digital Mammograms |
| title_short | Hybrid Intelligent Pattern Recognition Systems for Mass Segmentation and Classification: A Pilot Study on Full-Field Digital Mammograms |
| title_sort | hybrid intelligent pattern recognition systems for mass segmentation and classification a pilot study on full field digital mammograms |
| topic | hybrid intelligent system Fuzzy C-means adaptive neuro-fuzzy inference system K-means type-2 fuzzy sets image segmentation |
| url | https://www.mdpi.com/2076-3417/13/18/10401 |
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