Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images
<div><p>Breast cancer is the most menacing cancer among all types of cancer in women around the globe. Early diagnosis is the only way to increase the treatment options which then decreases the death rate and increases the chance of survival in patients. However, it is a challenging task...
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2022
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| _version_ | 1864513519137849344 |
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| author | Ahila A (18394806) |
| author2 | Poongodi M (18394809) Sami Bourouis (18394812) Shahab S. Band (11744695) Amir Mosavi (8694540) Shweta Agrawal (18394815) Mounir Hamdi (14150652) |
| author2_role | author author author author author author |
| author_facet | Ahila A (18394806) Poongodi M (18394809) Sami Bourouis (18394812) Shahab S. Band (11744695) Amir Mosavi (8694540) Shweta Agrawal (18394815) Mounir Hamdi (14150652) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ahila A (18394806) Poongodi M (18394809) Sami Bourouis (18394812) Shahab S. Band (11744695) Amir Mosavi (8694540) Shweta Agrawal (18394815) Mounir Hamdi (14150652) |
| dc.date.none.fl_str_mv | 2022-06-13T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3389/fonc.2022.834028 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Meta-Heuristic_Algorithm-Tuned_Neural_Network_for_Breast_Cancer_Diagnosis_Using_Ultrasound_Images/25624275 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Oncology and carcinogenesis breast cancer detection computer-aided diagnosis supervised learning texture features ultrasound imaging wavelet neural network |
| dc.title.none.fl_str_mv | Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <div><p>Breast cancer is the most menacing cancer among all types of cancer in women around the globe. Early diagnosis is the only way to increase the treatment options which then decreases the death rate and increases the chance of survival in patients. However, it is a challenging task to differentiate abnormal breast tissues from normal tissues because of their structure and unclear boundaries. Therefore, early and accurate diagnosis and classification of breast lesions into malignant or benign lesions is an active domain of research. Over the decade, numerous artificial neural network (ANN)-based techniques were adopted in order to diagnose and classify breast cancer due to the unique characteristics of learning key features from complex data via a training process. However, these schemes have limitations like slow convergence and longer training time. To address the above mentioned issues, this paper employs a meta-heuristic algorithm for tuning the parameters of the neural network. The main novelty of this work is the computer-aided diagnosis scheme for detecting abnormalities in breast ultrasound images by integrating a wavelet neural network (WNN) and the grey wolf optimization (GWO) algorithm. Here, breast ultrasound (US) images are preprocessed with a sigmoid filter followed by interference-based despeckling and then by anisotropic diffusion. The automatic segmentation algorithm is adopted to extract the region of interest, and subsequently morphological and texture features are computed. Finally, the GWO-tuned WNN is exploited to accomplish the classification task. The classification performance of the proposed scheme is validated on 346 ultrasound images. Efficiency of the proposed methodology is evaluated by computing the confusion matrix and receiver operating characteristic (ROC) curve. Numerical analysis revealed that the proposed work can yield higher classification accuracy when compared to the prevailing methods and thereby proves its potential in effective breast tumor detection and classification. The proposed GWO-WNN method (98%) gives better accuracy than other methods like SOM-SVM (87.5), LOFA-SVM (93.62%), MBA-RF (96.85%), and BAS-BPNN (96.3%)</p><p> </p></div><h2>Other Information</h2> <p> Published in: Frontiers in Oncology<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/fonc.2022.834028" target="_blank">https://dx.doi.org/10.3389/fonc.2022.834028</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_8aa7f83dc0703ab7dcc63bf05e5aea2e |
| identifier_str_mv | 10.3389/fonc.2022.834028 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25624275 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound ImagesAhila A (18394806)Poongodi M (18394809)Sami Bourouis (18394812)Shahab S. Band (11744695)Amir Mosavi (8694540)Shweta Agrawal (18394815)Mounir Hamdi (14150652)Biomedical and clinical sciencesOncology and carcinogenesisbreast cancer detectioncomputer-aided diagnosissupervised learningtexture featuresultrasound imagingwavelet neural network<div><p>Breast cancer is the most menacing cancer among all types of cancer in women around the globe. Early diagnosis is the only way to increase the treatment options which then decreases the death rate and increases the chance of survival in patients. However, it is a challenging task to differentiate abnormal breast tissues from normal tissues because of their structure and unclear boundaries. Therefore, early and accurate diagnosis and classification of breast lesions into malignant or benign lesions is an active domain of research. Over the decade, numerous artificial neural network (ANN)-based techniques were adopted in order to diagnose and classify breast cancer due to the unique characteristics of learning key features from complex data via a training process. However, these schemes have limitations like slow convergence and longer training time. To address the above mentioned issues, this paper employs a meta-heuristic algorithm for tuning the parameters of the neural network. The main novelty of this work is the computer-aided diagnosis scheme for detecting abnormalities in breast ultrasound images by integrating a wavelet neural network (WNN) and the grey wolf optimization (GWO) algorithm. Here, breast ultrasound (US) images are preprocessed with a sigmoid filter followed by interference-based despeckling and then by anisotropic diffusion. The automatic segmentation algorithm is adopted to extract the region of interest, and subsequently morphological and texture features are computed. Finally, the GWO-tuned WNN is exploited to accomplish the classification task. The classification performance of the proposed scheme is validated on 346 ultrasound images. Efficiency of the proposed methodology is evaluated by computing the confusion matrix and receiver operating characteristic (ROC) curve. Numerical analysis revealed that the proposed work can yield higher classification accuracy when compared to the prevailing methods and thereby proves its potential in effective breast tumor detection and classification. The proposed GWO-WNN method (98%) gives better accuracy than other methods like SOM-SVM (87.5), LOFA-SVM (93.62%), MBA-RF (96.85%), and BAS-BPNN (96.3%)</p><p> </p></div><h2>Other Information</h2> <p> Published in: Frontiers in Oncology<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/fonc.2022.834028" target="_blank">https://dx.doi.org/10.3389/fonc.2022.834028</a></p>2022-06-13T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3389/fonc.2022.834028https://figshare.com/articles/journal_contribution/Meta-Heuristic_Algorithm-Tuned_Neural_Network_for_Breast_Cancer_Diagnosis_Using_Ultrasound_Images/25624275CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/256242752022-06-13T03:00:00Z |
| spellingShingle | Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images Ahila A (18394806) Biomedical and clinical sciences Oncology and carcinogenesis breast cancer detection computer-aided diagnosis supervised learning texture features ultrasound imaging wavelet neural network |
| status_str | publishedVersion |
| title | Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images |
| title_full | Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images |
| title_fullStr | Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images |
| title_full_unstemmed | Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images |
| title_short | Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images |
| title_sort | Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images |
| topic | Biomedical and clinical sciences Oncology and carcinogenesis breast cancer detection computer-aided diagnosis supervised learning texture features ultrasound imaging wavelet neural network |