Machine Learning Enabled Early Detection of Breast Cancer by Structural Analysis of Mammograms  

作　　者：Mavra Mehmood Ember Ayub Fahad Ahmad Madallah Alruwaili Ziyad AAlrowaili Saad Alanazi Mamoona Humayun Muhammad Rizwan Shahid Naseem Tahir Alyas 

机构地区：[1]Department of Computer Sciences,Kinnaird College for Women,Lahore,54000,Pakistan [2]College of Computer and Information Sciences,Jouf University,Sakaka,Aljouf,72341,Saudi Arabia [3]Physics Department,College of Science,Jouf University,Sakaka,Aljouf,72341,Saudi Arabia [4]Division of Computer Science&Information Technology,University of Education,Lahore,54000,Pakistan [5]Department of Computer Science,Lahore Garrison University,Lahore,54000,Pakistan [6]Department of Basic Sciences,Deanship of Common First Year,Jouf University,Sakaka,Aljouf,72341,Saudi Arabia

出　　处：《Computers, Materials & Continua》2021年第4期641-657,共17页计算机、材料和连续体（英文）

摘　　要：Clinical image processing plays a signicant role in healthcare systems and is currently a widely used methodology.In carcinogenic diseases,time is crucial;thus,an image’s accurate analysis can help treat disease at an early stage.Ductal carcinoma in situ(DCIS)and lobular carcinoma in situ(LCIS)are common types of malignancies that affect both women and men.The number of cases of DCIS and LCIS has increased every year since 2002,while it still takes a considerable amount of time to recommend a controlling technique.Image processing is a powerful technique to analyze preprocessed images to retrieve useful information by using some remarkable processing operations.In this paper,we used a dataset from the Mammographic Image Analysis Society and MATLAB 2019b software from MathWorks to simulate and extract our results.In this proposed study,mammograms are primarily used to diagnose,more precisely,the breast’s tumor component.The detection of DCIS and LCIS on breast mammograms is done by preprocessing the images using contrast-limited adaptive histogram equalization.The resulting images’tumor portions are then isolated by a segmentation process,such as threshold detection.Furthermore,morphological operations,such as erosion and dilation,are applied to the images,then a gray-level co-occurrence matrix texture features,Harlick texture features,and shape features are extracted from the regions of interest.For classication purposes,a support vector machine(SVM)classier is used to categorize normal and abnormal patterns.Finally,the adaptive neuro-fuzzy inference system is deployed for the amputation of fuzziness due to overlapping features of patterns within the images,and the exact categorization of prior patterns is gained through the SVM.Early detection of DCIS and LCIS can save lives and help physicians and surgeons todiagnose and treat these diseases.Substantial results are obtained through cubic support vector machine(CSVM),respectively,showing 98.95%and 98.01%accuracies for normal and abnormal mammograms.Through

关 键 词：Image processing TUMOR segmentation DILATION EROSION machine learning classication support vector machine adaptive neuro-fuzzy inference system 

分 类 号：R73[医药卫生—肿瘤]