LBP–Bilateral Based Feature Fusion for Breast Cancer Diagnosis

作     者：Yassir Edrees Almalki Maida Khalid Sharifa Khalid Alduraibi Qudsia Yousaf Maryam Zaffar Shoayea Mohessen Almutiri Muhammad Irfan Mohammad Abd Alkhalik Basha Alaa Khalid Alduraibi Abdulrahman Manaa Alamri Khalaf Alshamrani Hassan A.Alshamrani 

作者机构：Division of RadiologyDepartment of MedicineMedical CollegeNajran UniversityNajran 61441Saudi Arabia Department of Computer Science and Information TechnologyIbadat International UniversityIslamabad44000Pakistan Department of RadiologyCollege of MedicineQassim UniversityBuraidah 52571Saudi Arabia Department of RadiologyKing Fahad Specialist HospitalBuraydah 52571Saudi Arabia Electrical Engineering DepartmentCollege of EngineeringNajran UniversityNajran 61441Saudi Arabia Radiology DepartmentHuman Medicine CollegeZagazig UniversityZagazig 44631Egypt Department of SurgeryCollege of MedicineNajran UniversityNajran 61441Saudi Arabia Radiological Sciences DepartmentCollege of Applied Medical SciencesNajran UniversityNajran 61441Saudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第73卷第11期

页      面：4103-4121页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：The authors would like to acknowledge the support of the Deputy for Research and Innovation—Ministry of Education Kingdom of Saudi Arabia for funding this research through a project grant code(NU/IFC/ENT/01/009)under the institutional Funding Committee at Najran University Kingdom of Saudi Arabia 

主　　题：Artificial intelligence machine learning breast cancer mammograms supervised learning classification feature fusion 

摘      要：Since reporting cases of breast cancer are on the rise all over the *** in regions such as Pakistan,Saudi Arabia,and the United *** methods for the early detection and diagnosis of breast cancer are *** usual diagnosis procedures followed by physicians has been updated with modern diagnostic approaches that include computer-aided support for better *** learning based practices has increased the accuracy and efficiency of medical diagnosis,which has helped save lives of many *** is much research in the field of medical imaging diagnostics that can be applied to the variety of data such as magnetic resonance images(MRIs),mammograms,X-rays,ultrasounds,and histopathological images,but magnetic resonance(MR)and mammogram imaging have proved to present the promising *** proposed paper has presented the results of classification algorithms over Breast Cancer(BC)mammograms from a novel dataset taken from hospitals in the Qassim health cluster of Saudi *** paper has developed a novel approach called the novel spectral extraction algorithm(NSEA)that uses feature extraction and fusion by using local binary pattern(LBP)and bilateral algorithms,as well as a support vector machine(SVM)as a *** NSEA with the SVM classifier demonstrated a promising accuracy of 94%and an elapsed time of 0.68 milliseconds,which were significantly better results than those of comparative experiments from classifiers named Naïve Bayes,logistic regression,K-Nearest Neighbor(KNN),Gaussian Discriminant Analysis(GDA),AdaBoost and Extreme Learning Machine(ELM).ELM produced the comparative accuracy of 94%however has a lower elapsed time of 1.35 as compared to *** has produced a fairly well accuracy of 82%,KNN has a low accuracy of 66%.However Logistic Regression,GDA and Naïve Bayes have produced the lowest accuracies of 47%,43%and 42%.