Control experiments on the BUSI dataset.
<div><p>Breast cancer is the most common cancer in women. Breast masses are one of the distinctive signs for diagnosing breast cancer, and ultrasound is widely used for screening as a non-invasive and effective method for breast examination. In this study, we used the Mendeley and BUSI d...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2024
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| _version_ | 1852025473887895552 |
|---|---|
| author | Hao Zhang (15339) |
| author2 | He Liang (5151095) Guo Wenjia (20036991) Ma Jing (17687604) Sun Gang (20036994) Ma Hongbing (20036997) |
| author2_role | author author author author author |
| author_facet | Hao Zhang (15339) He Liang (5151095) Guo Wenjia (20036991) Ma Jing (17687604) Sun Gang (20036994) Ma Hongbing (20036997) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hao Zhang (15339) He Liang (5151095) Guo Wenjia (20036991) Ma Jing (17687604) Sun Gang (20036994) Ma Hongbing (20036997) |
| dc.date.none.fl_str_mv | 2024-11-01T17:32:14Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0307916.t004 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Control_experiments_on_the_BUSI_dataset_/27471939 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Cell Biology Cancer Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified guiding clinical decision feature extraction capabilities experimental results demonstrate dice similarity coefficient deep learning approaches comprising 250 images three attention modules scale attention modules channel attention modules densely connected network deep learning method div >< p diagnosing breast cancer breast ultrasound images breast cancer 780 images multiple attention attention gates network ’ breast masses breast examination method achieves effective method common cancer source code relevant features mass segmentation encoding stage distinctive signs decoding stage crucial role better capture accurate measurement 487 benign 150 malignant 133 normal 100 benign |
| dc.title.none.fl_str_mv | Control experiments on the BUSI dataset. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>Breast cancer is the most common cancer in women. Breast masses are one of the distinctive signs for diagnosing breast cancer, and ultrasound is widely used for screening as a non-invasive and effective method for breast examination. In this study, we used the Mendeley and BUSI datasets, comprising 250 images (100 benign, 150 malignant) and 780 images (133 normal, 487 benign, 210 malignant), respectively. The datasets were split into 80% for training and 20% for validation. The accurate measurement and characterization of different breast tumors play a crucial role in guiding clinical decision-making. The area and shape of the different breast tumors detected are critical for clinicians to make accurate diagnostic decisions. In this study, a deep learning method for mass segmentation in breast ultrasound images is proposed, which uses densely connected U-net with attention gates (AGs) as well as channel attention modules and scale attention modules for accurate breast tumor segmentation.The densely connected network is employed in the encoding stage to enhance the network’s feature extraction capabilities. Three attention modules are integrated in the decoding stage to better capture the most relevant features. After validation on the Mendeley and BUSI datasets, the experimental results demonstrate that our method achieves a Dice Similarity Coefficient (DSC) of 0.8764 and 0.8313, respectively, outperforming other deep learning approaches. The source code is located at <a href="http://github.com/zhanghaoCV/plos-one" target="_blank">github.com/zhanghaoCV/plos-one</a>.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_de3ff3d693eced5b4cf850d795d233bb |
| identifier_str_mv | 10.1371/journal.pone.0307916.t004 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27471939 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Control experiments on the BUSI dataset.Hao Zhang (15339)He Liang (5151095)Guo Wenjia (20036991)Ma Jing (17687604)Sun Gang (20036994)Ma Hongbing (20036997)Cell BiologyCancerSpace ScienceBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedguiding clinical decisionfeature extraction capabilitiesexperimental results demonstratedice similarity coefficientdeep learning approachescomprising 250 imagesthree attention modulesscale attention moduleschannel attention modulesdensely connected networkdeep learning methoddiv >< pdiagnosing breast cancerbreast ultrasound imagesbreast cancer780 imagesmultiple attentionattention gatesnetwork ’breast massesbreast examinationmethod achieveseffective methodcommon cancersource coderelevant featuresmass segmentationencoding stagedistinctive signsdecoding stagecrucial rolebetter captureaccurate measurement487 benign150 malignant133 normal100 benign<div><p>Breast cancer is the most common cancer in women. Breast masses are one of the distinctive signs for diagnosing breast cancer, and ultrasound is widely used for screening as a non-invasive and effective method for breast examination. In this study, we used the Mendeley and BUSI datasets, comprising 250 images (100 benign, 150 malignant) and 780 images (133 normal, 487 benign, 210 malignant), respectively. The datasets were split into 80% for training and 20% for validation. The accurate measurement and characterization of different breast tumors play a crucial role in guiding clinical decision-making. The area and shape of the different breast tumors detected are critical for clinicians to make accurate diagnostic decisions. In this study, a deep learning method for mass segmentation in breast ultrasound images is proposed, which uses densely connected U-net with attention gates (AGs) as well as channel attention modules and scale attention modules for accurate breast tumor segmentation.The densely connected network is employed in the encoding stage to enhance the network’s feature extraction capabilities. Three attention modules are integrated in the decoding stage to better capture the most relevant features. After validation on the Mendeley and BUSI datasets, the experimental results demonstrate that our method achieves a Dice Similarity Coefficient (DSC) of 0.8764 and 0.8313, respectively, outperforming other deep learning approaches. The source code is located at <a href="http://github.com/zhanghaoCV/plos-one" target="_blank">github.com/zhanghaoCV/plos-one</a>.</p></div>2024-11-01T17:32:14ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0307916.t004https://figshare.com/articles/dataset/Control_experiments_on_the_BUSI_dataset_/27471939CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/274719392024-11-01T17:32:14Z |
| spellingShingle | Control experiments on the BUSI dataset. Hao Zhang (15339) Cell Biology Cancer Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified guiding clinical decision feature extraction capabilities experimental results demonstrate dice similarity coefficient deep learning approaches comprising 250 images three attention modules scale attention modules channel attention modules densely connected network deep learning method div >< p diagnosing breast cancer breast ultrasound images breast cancer 780 images multiple attention attention gates network ’ breast masses breast examination method achieves effective method common cancer source code relevant features mass segmentation encoding stage distinctive signs decoding stage crucial role better capture accurate measurement 487 benign 150 malignant 133 normal 100 benign |
| status_str | publishedVersion |
| title | Control experiments on the BUSI dataset. |
| title_full | Control experiments on the BUSI dataset. |
| title_fullStr | Control experiments on the BUSI dataset. |
| title_full_unstemmed | Control experiments on the BUSI dataset. |
| title_short | Control experiments on the BUSI dataset. |
| title_sort | Control experiments on the BUSI dataset. |
| topic | Cell Biology Cancer Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified guiding clinical decision feature extraction capabilities experimental results demonstrate dice similarity coefficient deep learning approaches comprising 250 images three attention modules scale attention modules channel attention modules densely connected network deep learning method div >< p diagnosing breast cancer breast ultrasound images breast cancer 780 images multiple attention attention gates network ’ breast masses breast examination method achieves effective method common cancer source code relevant features mass segmentation encoding stage distinctive signs decoding stage crucial role better capture accurate measurement 487 benign 150 malignant 133 normal 100 benign |