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 

机构地区：[1]Division of Radiology,Department of Medicine,Medical College,Najran University,Najran 61441,Saudi Arabia [2]Department of Computer Science and Information Technology,Ibadat International University,Islamabad,44000,Pakistan [3]Department of Radiology,College of Medicine,Qassim University,Buraidah 52571,Saudi Arabia [4]Department of Radiology,King Fahad Specialist Hospital,Buraydah 52571,Saudi Arabia [5]Electrical Engineering Department,College of Engineering,Najran University,Najran 61441,Saudi Arabia [6]Radiology Department,Human Medicine College,Zagazig University,Zagazig 44631,Egypt [7]Department of Surgery,College of Medicine,Najran University,Najran 61441,Saudi Arabia [8]Radiological Sciences Department,College of Applied Medical Sciences,Najran University,Najran 61441,Saudi Arabia

出　　处：《Computers, Materials & Continua》2022年第11期4103-4121,共19页计算机、材料和连续体（英文）

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.

摘　　要：Since reporting cases of breast cancer are on the rise all over the world.Especially in regions such as Pakistan,Saudi Arabia,and the United States.Efficient methods for the early detection and diagnosis of breast cancer are needed.The usual diagnosis procedures followed by physicians has been updated with modern diagnostic approaches that include computer-aided support for better accuracy.Machine learning based practices has increased the accuracy and efficiency of medical diagnosis,which has helped save lives of many patients.There 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 results.The 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 Arabia.This 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 classifier.The 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 SVM.Adaboost 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%.

关 键 词：Artificial intelligence machine learning breast cancer MAMMOGRAMS supervised learning CLASSIFICATION feature fusion 

分 类 号：R730.4[医药卫生—肿瘤]