Recognizing Breast Cancer Using Edge-Weighted Texture Features of Histopathology Images
Around one in eight women will be diagnosed with breast cancer at some time. Improved patient outcomes necessitate both early detection and an accurate diagnosis. Histological images are routinely utilized in the process of diagnosing breast cancer. Methods proposed in recent research only focus on...
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| Veröffentlicht in: | Computers, materials & continua Jg. 77; H. 1; S. 1081 - 1101 |
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| Sprache: | Englisch |
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2023
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| Abstract | Around one in eight women will be diagnosed with breast cancer at some time. Improved patient outcomes necessitate both early detection and an accurate diagnosis. Histological images are routinely utilized in the process of diagnosing breast cancer. Methods proposed in recent research only focus on classifying breast cancer on specific magnification levels. No study has focused on using a combined dataset with multiple magnification levels to classify breast cancer. A strategy for detecting breast cancer is provided in the context of this investigation. Histopathology image texture data is used with the wavelet transform in this technique. The proposed method comprises converting histopathological images from Red Green Blue (RGB) to Chrominance of Blue and Chrominance of Red (YCBCR), utilizing a wavelet transform to extract texture information, and classifying the images with Extreme Gradient Boosting (XGBOOST). Furthermore, SMOTE has been used for resampling as the dataset has imbalanced samples. The suggested method is evaluated using 10-fold cross-validation and achieves an accuracy of 99.27% on the BreakHis 1.0 40X dataset, 98.95% on the BreakHis 1.0 100X dataset, 98.92% on the BreakHis 1.0 200X dataset, 98.78% on the BreakHis 1.0 400X dataset, and 98.80% on the combined dataset. The findings of this study imply that improved breast cancer detection rates and patient outcomes can be achieved by combining wavelet transformation with textural signals to detect breast cancer in histopathology images. |
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| AbstractList | Around one in eight women will be diagnosed with breast cancer at some time. Improved patient outcomes necessitate both early detection and an accurate diagnosis. Histological images are routinely utilized in the process of diagnosing breast cancer. Methods proposed in recent research only focus on classifying breast cancer on specific magnification levels. No study has focused on using a combined dataset with multiple magnification levels to classify breast cancer. A strategy for detecting breast cancer is provided in the context of this investigation. Histopathology image texture data is used with the wavelet transform in this technique. The proposed method comprises converting histopathological images from Red Green Blue (RGB) to Chrominance of Blue and Chrominance of Red (YCBCR), utilizing a wavelet transform to extract texture information, and classifying the images with Extreme Gradient Boosting (XGBOOST). Furthermore, SMOTE has been used for resampling as the dataset has imbalanced samples. The suggested method is evaluated using 10-fold cross-validation and achieves an accuracy of 99.27% on the BreakHis 1.0 40X dataset, 98.95% on the BreakHis 1.0 100X dataset, 98.92% on the BreakHis 1.0 200X dataset, 98.78% on the BreakHis 1.0 400X dataset, and 98.80% on the combined dataset. The findings of this study imply that improved breast cancer detection rates and patient outcomes can be achieved by combining wavelet transformation with textural signals to detect breast cancer in histopathology images. |
| Author | Akram, Arslan |
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| Cites_doi | 10.1080/03772063.2019.1583610 10.5815/ijigsp.2012.10.05 10.1613/jair.953 10.1108/WJE-09-2020-0456 10.1016/j.iswa.2022.200066 10.1155/2022/8904768 10.1109/TMI.2016.2525803 10.1038/s41598-022-19112-9 10.3390/s20164373 10.1038/srep46450 10.1093/eurpub/ckz216 10.1155/2023/4597445 10.1002/ima.22465 10.3390/math10214109 10.1093/bioinformatics/btac267 10.1080/23808993.2019.1585805 10.1016/j.ipm.2020.102439 10.1016/j.icte.2021.11.010 10.32604/cmc.2023.032005 10.3390/app13010156 10.17762/ijritcc.v10i4.5532 10.1016/j.bspc.2021.103212 10.1155/2021/8396438 10.1371/journal.pone.0267955 10.1166/jmihi.2019.2648 10.32604/cmc.2023.035287 10.1016/j.media.2004.06.007 |
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| SubjectTerms | Breast cancer Classification Clinical outcomes Datasets Histopathology Medical imaging Resampling Texture recognition Wavelet transforms |
| Title | Recognizing Breast Cancer Using Edge-Weighted Texture Features of Histopathology Images |
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