Automated breast cancer detection by reconstruction independent component analysis (RICA) based hybrid features using machine learning paradigms
Breast cancer (BC) masses and microcalcification are nonlinear with complex dynamics due to which radiologists fail to properly diagnose breast cancer. In this paper, we used a hybrid features extracting approach based on texture, morphological, Scale Invariant Feature Transform (SIFT), Gray Level C...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2022.2151566 |
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| author | Lal Hussain Shahzad Ahmad Qureshi Amjad Aldweesh Jawad ur Rehman Pirzada Faisal Mehmood Butt Elsayed Tag eldin Mushtaq Ali Abdulmohsen Algarni Muhammad Amin Nadim |
| author_facet | Lal Hussain Shahzad Ahmad Qureshi Amjad Aldweesh Jawad ur Rehman Pirzada Faisal Mehmood Butt Elsayed Tag eldin Mushtaq Ali Abdulmohsen Algarni Muhammad Amin Nadim |
| author_sort | Lal Hussain |
| collection | DOAJ |
| description | Breast cancer (BC) masses and microcalcification are nonlinear with complex dynamics due to which radiologists fail to properly diagnose breast cancer. In this paper, we used a hybrid features extracting approach based on texture, morphological, Scale Invariant Feature Transform (SIFT), Gray Level Co-occurrence Matrix (GLCM), entropy, Elliptic Fourier Descriptors (EFDs), RICA, and sparse filtering methods. Various machine learning techniques have been employed to detect breast cancer, viz. Support Vector Machines (SVM), Decision Trees (DT), k-Nearest Neighbour, and Naïve Bayes classifiers. The RICA-based feature set using SVM RBF has resulted in total accuracy of (94.88%), and ROC AUC = 0.9914. The hybrid features using RICA have been computed with other combinatorial logics. Moreover, the highest performance to detect BC based on the fusion of features was obtained with RICA with Textural features using SVM Gaussian kernel and yielded a total accuracy of (97.55%), and ROC AUC = 0.9976. The hybrid features with RICA were found to yield the highest detection performance. It is revealed that the new feature-extracting approach can be useful for the early detection of breast cancer by physicians to decrease the overall mortality rate. The methods will be very useful for treatment modification to achieve better clinical outcomes. |
| format | Article |
| id | doaj-art-cfffd33d60ad488bb2385df17c70b911 |
| institution | Kabale University |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-cfffd33d60ad488bb2385df17c70b9112025-08-20T03:24:55ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-013412784280610.1080/09540091.2022.21515662151566Automated breast cancer detection by reconstruction independent component analysis (RICA) based hybrid features using machine learning paradigmsLal Hussain0Shahzad Ahmad Qureshi1Amjad Aldweesh2Jawad ur Rehman Pirzada3Faisal Mehmood Butt4Elsayed Tag eldin5Mushtaq Ali6Abdulmohsen Algarni7Muhammad Amin Nadim8Department of Computer Science & IT, Neelum Campus, The University of Azad Jammu and KashmirDepartment of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS)College of Computer science and information technology, Shaqra UniversityDepartment of Computer Science & IT, Neelum Campus, The University of Azad Jammu and KashmirDepartment of Electrical Engineering, The University of Azad Jammu and KashmirFaculty of Engineering and Technology, Future University in EgyptDepartment of Computer Science & Information Technology, Hazara UniversityCollege of Computer Science, King Khalid UniversityDepartment of Computer Science, University of South AsiaBreast cancer (BC) masses and microcalcification are nonlinear with complex dynamics due to which radiologists fail to properly diagnose breast cancer. In this paper, we used a hybrid features extracting approach based on texture, morphological, Scale Invariant Feature Transform (SIFT), Gray Level Co-occurrence Matrix (GLCM), entropy, Elliptic Fourier Descriptors (EFDs), RICA, and sparse filtering methods. Various machine learning techniques have been employed to detect breast cancer, viz. Support Vector Machines (SVM), Decision Trees (DT), k-Nearest Neighbour, and Naïve Bayes classifiers. The RICA-based feature set using SVM RBF has resulted in total accuracy of (94.88%), and ROC AUC = 0.9914. The hybrid features using RICA have been computed with other combinatorial logics. Moreover, the highest performance to detect BC based on the fusion of features was obtained with RICA with Textural features using SVM Gaussian kernel and yielded a total accuracy of (97.55%), and ROC AUC = 0.9976. The hybrid features with RICA were found to yield the highest detection performance. It is revealed that the new feature-extracting approach can be useful for the early detection of breast cancer by physicians to decrease the overall mortality rate. The methods will be very useful for treatment modification to achieve better clinical outcomes.http://dx.doi.org/10.1080/09540091.2022.2151566breast cancersupport vector machinebayesian approachrica and sparse filters |
| spellingShingle | Lal Hussain Shahzad Ahmad Qureshi Amjad Aldweesh Jawad ur Rehman Pirzada Faisal Mehmood Butt Elsayed Tag eldin Mushtaq Ali Abdulmohsen Algarni Muhammad Amin Nadim Automated breast cancer detection by reconstruction independent component analysis (RICA) based hybrid features using machine learning paradigms Connection Science breast cancer support vector machine bayesian approach rica and sparse filters |
| title | Automated breast cancer detection by reconstruction independent component analysis (RICA) based hybrid features using machine learning paradigms |
| title_full | Automated breast cancer detection by reconstruction independent component analysis (RICA) based hybrid features using machine learning paradigms |
| title_fullStr | Automated breast cancer detection by reconstruction independent component analysis (RICA) based hybrid features using machine learning paradigms |
| title_full_unstemmed | Automated breast cancer detection by reconstruction independent component analysis (RICA) based hybrid features using machine learning paradigms |
| title_short | Automated breast cancer detection by reconstruction independent component analysis (RICA) based hybrid features using machine learning paradigms |
| title_sort | automated breast cancer detection by reconstruction independent component analysis rica based hybrid features using machine learning paradigms |
| topic | breast cancer support vector machine bayesian approach rica and sparse filters |
| url | http://dx.doi.org/10.1080/09540091.2022.2151566 |
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