{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T18:27:20Z","timestamp":1771266440734,"version":"3.50.1"},"reference-count":64,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,9,20]],"date-time":"2022-09-20T00:00:00Z","timestamp":1663632000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2022,9,20]]},"abstract":"<jats:p>Breast cancer disease is one of the most recorded cancers that lead to morbidity and maybe death among women around the world. Recent research statistics have exposed that one from 8 females in the USA and one from 10 females in Europe are contaminated by breast cancer. The challenge with this disease is how to develop a relaxed and fast diagnosing method. One of the attractive ways of early breast cancer diagnosis is based on the mammogram images analysis of the breast using a computer-aided diagnosing (CAD) tool. This paper firstly aimed to propose an efficient method for diagnosing tumors based on mammogram images of breasts using a machine learning approach. Secondly, this paper aimed to the development of a CAD software program for breast cancer diagnosing based on the proposed method in the first step. The followed step-by-step procedure of the proposed method is performed by passing the Mammographic Image Analysis Society (MIAS) through five steps of image preprocessing, image segmentation using seeded region growing (SRG) algorithm, feature extraction using different feature\u2019s extraction classes, and important and effectiveness feature selection using the Sequential Forward Selection (SFS) technique, and finally, the Support Vector Machine (SVM) algorithm is used as a binary classifier in two classification levels. The first level classifier is used to categorize the given image as normal or abnormal while the second-level classifier is used for further classifying the abnormal image as either a malignant or benign cancer. The proposed method is studied and investigated in two phases: the training phase and the testing phase, with the MIAS dataset of mammogram images, using 70% and 30% ratios of dataset images for the training and testing sets, respectively. The practical implementation of the proposed method and the graphical user interface (GUI) CAD tool are carried out using MATLAB software. Experimental results of the proposed method have shown that the accuracy of the proposed method reached 100% in classifying images as normal and abnormal mammogram images while the classification accuracy for benign and malignant is equal to 87.1%.<\/jats:p>","DOI":"10.1155\/2022\/3895976","type":"journal-article","created":{"date-parts":[[2022,9,20]],"date-time":"2022-09-20T18:35:36Z","timestamp":1663698936000},"page":"1-17","source":"Crossref","is-referenced-by-count":14,"title":["A Diagnostic Model of Breast Cancer Based on Digital Mammogram Images Using Machine Learning Techniques"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8451-4241","authenticated-orcid":true,"given":"Farouk A. K.","family":"Al-Fahaidy","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Electrical Engineering Department, IBB University, IBB, Yemen"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7912-8629","authenticated-orcid":true,"given":"Belal","family":"Al-Fuhaidi","sequence":"additional","affiliation":[{"name":"Faculty of Computing and IT, University of Science and Technology Sana\u2019a, Taiz, Yemen"}]},{"given":"Ishaq","family":"AL-Darouby","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Electrical Engineering Department, IBB University, IBB, Yemen"}]},{"given":"Faheem","family":"AL-Abady","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Electrical Engineering Department, IBB University, IBB, Yemen"}]},{"given":"Mohammed","family":"AL-Qadry","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Electrical Engineering Department, IBB University, IBB, Yemen"}]},{"given":"Abdurhman","family":"AL-Gamal","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Electrical Engineering Department, IBB University, IBB, Yemen"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"crossref","article-title":"Automatically density based breast segmentation for mammograms by using dynamic k-means algorithm and seed based region growing","author":"A. Elmoufidi","DOI":"10.1109\/I2MTC.2015.7151324"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1109\/tim.2010.2051060"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2016.10.026"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1504\/ijbet.2019.10018402"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1109\/IMTC.2010.5488124"},{"key":"6","volume-title":"American College of Radiology Breast Imaging Reporting and Data System (BIRADS)","author":"American College of Radiology","year":"2003"},{"key":"7","doi-asserted-by":"crossref","DOI":"10.1016\/j.clinimag.2012.09.024","article-title":"Computer-aided detection\/diagnosis of breast cancer in mammography and ultrasound","volume":"37","author":"A. Jalalian","year":"2013","journal-title":"Clinical Imaging"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1109\/MIM.2014.6825388"},{"key":"9","article-title":"Mammographic image analysis society (mias) database v1. 21","author":"J. Suckling","year":"2015"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1109\/IECBES.2010.5742205"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1109\/34.295913"},{"key":"12","article-title":"An integrated interactive technique for image segmentation using stack based seeded region growing and thresholding","volume":"6","author":"S. Hore","year":"2016","journal-title":"International Journal of Electrical and Computer Engineering"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1504\/ijbet.2018.10024767"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.17762\/turcomat.v12i2.2392"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-09905-3"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1504\/ijcaet.2021.114495"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-022-01071-7"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCCI48352.2020.9104154"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1016\/j.matpr.2020.08.543"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/9162464"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1109\/tbme.2014.2303852"},{"key":"22","article-title":"Automatic breast density classification on tomosynthesis images","author":"M. S. R. M. Sim\u00f5es","year":"2021","journal-title":"Dissent"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1155\/2007\/49482"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1109\/NGNS.2014.6990239"},{"key":"25","article-title":"Detection of regions of Interest in mammograms by using local binary pattern and dynamic K-means algorithm","volume":"1","author":"A. Elmoufidi","year":"2014","journal-title":"International Journal of Image and Video Processing: Theory and Application"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.14569\/ijacsa.2016.070568"},{"key":"27","article-title":"CAD based system for automatic detection et classification of suspicious lesions in mammograms","volume":"3","author":"U. K. Veena","year":"2014","journal-title":"International Journal of Emerging Trends et Technology in Computer Science (IJETTCS)"},{"key":"28","article-title":"Classification of breast masses in digital mammograms using support vector machines","volume":"3","author":"N. M. Basheer","year":"2013","journal-title":"International Journal of Advanced Research in Computer Science and Software Engineering"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-05697-1"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.1007\/s11265-008-0198-2"},{"key":"31","doi-asserted-by":"publisher","DOI":"10.1007\/s11045-020-00756-7"},{"key":"32","doi-asserted-by":"publisher","DOI":"10.1111\/coin.12522"},{"key":"33","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2018.01.017"},{"key":"34","doi-asserted-by":"publisher","DOI":"10.1109\/titb.2010.2043296"},{"key":"35","doi-asserted-by":"publisher","DOI":"10.5772\/intechopen.69792"},{"key":"36","doi-asserted-by":"publisher","DOI":"10.1080\/08839514.2021.2001177"},{"key":"37","doi-asserted-by":"crossref","article-title":"Textural features-based computer aided diagnostic system for mammogram mass classification","author":"J. A. Jaleel","DOI":"10.1109\/ICCICCT.2014.6993069"},{"key":"38","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2019.100239"},{"key":"39","article-title":"Analysis of tissue abnormality in mammography images using gray level co-occurrence matrix method","volume-title":"Journal of Physics: Conference Series","author":"M. Y. Kamil","year":"2020"},{"key":"40","doi-asserted-by":"publisher","DOI":"10.1016\/j.amsu.2020.12.043"},{"key":"41","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0161501"},{"key":"42","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2016.04.224"},{"issue":"5","key":"43","article-title":"Breast cancer prediction using machine learning","volume":"7","author":"R. Rawal","year":"2020","journal-title":"Journal of Emerging Technologies and Innovative Research"},{"key":"44","doi-asserted-by":"publisher","DOI":"10.1109\/ICECOCS.2018.8610632"},{"key":"45","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/2717454"},{"key":"46","doi-asserted-by":"publisher","DOI":"10.1117\/1.jmi.6.3.031405"},{"key":"47","doi-asserted-by":"publisher","DOI":"10.3390\/app112412122"},{"key":"48","volume-title":"Digital Image Processing","author":"R. C. Gonzalez","year":"2009"},{"key":"49","doi-asserted-by":"crossref","DOI":"10.1201\/b15731","volume-title":"Introduction to Digital Image Processing","author":"W. K. Pratt","year":"2013"},{"key":"50","article-title":"Computer vision approaches to medical image analysis","volume-title":"Lecture Notes in, Computer Science","author":"R. Beichel","year":"2006"},{"key":"51","doi-asserted-by":"publisher","DOI":"10.1109\/proc.1979.11328"},{"key":"52","article-title":"Improving Co-occurrence matrix feature discrimination","author":"R. F. Walker"},{"key":"53","doi-asserted-by":"publisher","DOI":"10.1080\/09720529.2019.1642624"},{"key":"54","first-page":"63","volume-title":"Medical Imaging Systems Technology Analysis and Computational Methods","author":"C. T. Leondes","year":"2005"},{"key":"55","doi-asserted-by":"publisher","DOI":"10.32604\/sdhm.2021.012751"},{"key":"56","doi-asserted-by":"publisher","DOI":"10.1504\/ijbet.2015.071405"},{"key":"57","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1016\/j.patcog.2005.07.006","article-title":"Approaches for automated detection and classification of masses in mammograms","volume":"39","author":"H. D. Cheng","year":"2006","journal-title":"Pattern Recognition Society"},{"key":"58","article-title":"Mammogram computer aided diagnosis","volume":"5","author":"B. K. Elfarra","year":"2012","journal-title":"International Journal of Signal Processing, Image Processing and Pattern Recognition"},{"key":"59","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/6614730"},{"key":"60","doi-asserted-by":"publisher","DOI":"10.1109\/tim.2013.2278562"},{"key":"61","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsv.2015.11.008"},{"key":"62","doi-asserted-by":"publisher","DOI":"10.1109\/tim.2010.2040905"},{"key":"63","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2017.12.001"},{"key":"64","article-title":"A study on several machine-learning methods for classification of malignant and benignclustered microcalcifications","volume":"24","author":"L. Wei","year":"2005","journal-title":"Medical Imaging"}],"container-title":["Applied Computational Intelligence and Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/acisc\/2022\/3895976.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/acisc\/2022\/3895976.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/acisc\/2022\/3895976.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,20]],"date-time":"2022-09-20T18:35:54Z","timestamp":1663698954000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/acisc\/2022\/3895976\/"}},"subtitle":[],"editor":[{"given":"V. E.","family":"Sathishkumar","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,9,20]]},"references-count":64,"alternative-id":["3895976","3895976"],"URL":"https:\/\/doi.org\/10.1155\/2022\/3895976","relation":{},"ISSN":["1687-9732","1687-9724"],"issn-type":[{"value":"1687-9732","type":"electronic"},{"value":"1687-9724","type":"print"}],"subject":[],"published":{"date-parts":[[2022,9,20]]}}}