Gaussian Optimized Deep Learning-based Belief Classification Model for Breast Cancer Detection

作     者：Areej A.Malibari Marwa Obayya Mohamed K.Nour Amal S.Mehanna Manar Ahmed Hamza Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel 

作者机构：Department of Industrial and Systems EngineeringCollege of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Biomedical EngineeringCollege of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer SciencesCollege of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Digital MediaFaculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self DevelopmentPreparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi 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：4123-4138页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 1002[医学-临床医学] 0809[工学-电子科学与技术（可授工学、理学学位）] 0805[工学-材料科学与工程（可授工学、理学学位）] 100214[医学-肿瘤学] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 10[医学] 

基　　金：Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R151) Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR12) 

主　　题：Breast cancer detection computer-aided diagnosis(CAD) deep learning CNN entropy butterfly optimization 

摘      要：With the rapid increase of new cases with an increased mortality rate,cancer is considered the second and most deadly disease *** cancer is the most widely affected cancer worldwide,with an increased death rate *** to radiologists’processing of mammogram images,many computer-aided diagnoses have been developed to detect breast *** detection of breast cancer will reduce the death rate *** early diagnosis of breast cancer using the developed computer-aided diagnosis(CAD)systems still needed to be enhanced by incorporating innovative deep learning technologies to improve the accuracy and sensitivity of the detection system with a reduced false positive *** paper proposed an efficient and optimized deep learning-based feature selection approach with this *** model selects the relevant features from the mammogram images that can improve the accuracy of malignant detection and reduce the false alarm *** learning is used in the extraction of features *** ext,a convolution neural network,is used to extract the *** two feature vectors are fused and optimized with enhanced Butterfly Optimization with Gaussian function(TL-CNN-EBOG)to select the final most relevant *** optimized features are applied to the classifier called Deep belief network(DBN)to classify the benign and malignant *** feature extraction and classification process used two datasets,breast,and *** to the existing methods,the optimized deep learning-based model secured 98.6%of improved accuracy on the breast dataset and 98.85%of improved accuracy on the MIAS dataset.