A Comprehensive Review of Computer-Aided Models for Breast Cancer Diagnosis Using Histopathology Images
Breast cancer is the second most common cancer in women who are mainly middle-aged and older. The American Cancer Society reported that the average risk of developing breast cancer sometime in their life is about 13%, and this incident rate has increased by 0.5% per year in recent years. A biopsy is...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Bioengineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5354/10/11/1289 |
| _version_ | 1827640513207992320 |
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| author | Alberto Labrada Buket D. Barkana |
| author_facet | Alberto Labrada Buket D. Barkana |
| author_sort | Alberto Labrada |
| collection | DOAJ |
| description | Breast cancer is the second most common cancer in women who are mainly middle-aged and older. The American Cancer Society reported that the average risk of developing breast cancer sometime in their life is about 13%, and this incident rate has increased by 0.5% per year in recent years. A biopsy is done when screening tests and imaging results show suspicious breast changes. Advancements in computer-aided system capabilities and performance have fueled research using histopathology images in cancer diagnosis. Advances in machine learning and deep neural networks have tremendously increased the number of studies developing computerized detection and classification models. The dataset-dependent nature and trial-and-error approach of the deep networks’ performance produced varying results in the literature. This work comprehensively reviews the studies published between 2010 and 2022 regarding commonly used public-domain datasets and methodologies used in preprocessing, segmentation, feature engineering, machine-learning approaches, classifiers, and performance metrics. |
| first_indexed | 2024-03-09T17:00:43Z |
| format | Article |
| id | doaj.art-83beed993b6a43a7b6dc1576e6175caf |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-09T17:00:43Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-83beed993b6a43a7b6dc1576e6175caf2023-11-24T14:29:50ZengMDPI AGBioengineering2306-53542023-11-011011128910.3390/bioengineering10111289A Comprehensive Review of Computer-Aided Models for Breast Cancer Diagnosis Using Histopathology ImagesAlberto Labrada0Buket D. Barkana1Department of Electrical Engineering, The University of Bridgeport, Bridgeport, CT 06604, USADepartment of Biomedical Engineering, The University of Akron, Akron, OH 44325, USABreast cancer is the second most common cancer in women who are mainly middle-aged and older. The American Cancer Society reported that the average risk of developing breast cancer sometime in their life is about 13%, and this incident rate has increased by 0.5% per year in recent years. A biopsy is done when screening tests and imaging results show suspicious breast changes. Advancements in computer-aided system capabilities and performance have fueled research using histopathology images in cancer diagnosis. Advances in machine learning and deep neural networks have tremendously increased the number of studies developing computerized detection and classification models. The dataset-dependent nature and trial-and-error approach of the deep networks’ performance produced varying results in the literature. This work comprehensively reviews the studies published between 2010 and 2022 regarding commonly used public-domain datasets and methodologies used in preprocessing, segmentation, feature engineering, machine-learning approaches, classifiers, and performance metrics.https://www.mdpi.com/2306-5354/10/11/1289reviewbreast cancercomputer-aided diagnosis (CAD)machine learningdeformable modesclassification |
| spellingShingle | Alberto Labrada Buket D. Barkana A Comprehensive Review of Computer-Aided Models for Breast Cancer Diagnosis Using Histopathology Images Bioengineering review breast cancer computer-aided diagnosis (CAD) machine learning deformable modes classification |
| title | A Comprehensive Review of Computer-Aided Models for Breast Cancer Diagnosis Using Histopathology Images |
| title_full | A Comprehensive Review of Computer-Aided Models for Breast Cancer Diagnosis Using Histopathology Images |
| title_fullStr | A Comprehensive Review of Computer-Aided Models for Breast Cancer Diagnosis Using Histopathology Images |
| title_full_unstemmed | A Comprehensive Review of Computer-Aided Models for Breast Cancer Diagnosis Using Histopathology Images |
| title_short | A Comprehensive Review of Computer-Aided Models for Breast Cancer Diagnosis Using Histopathology Images |
| title_sort | comprehensive review of computer aided models for breast cancer diagnosis using histopathology images |
| topic | review breast cancer computer-aided diagnosis (CAD) machine learning deformable modes classification |
| url | https://www.mdpi.com/2306-5354/10/11/1289 |
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