{"title":"Breast Cancer Diagnosis Using Computational Model: Recent Advancement","authors":"Rishav Sharma, R. Malviya, Prerna Uniyal","doi":"10.2174/0115733947288059240405041146","DOIUrl":null,"url":null,"abstract":"\n\nSince breast cancer affects one in every four women, it is of the utmost need to investigate\nnovel diagnostic technologies and treatment techniques. This requires the development of diagnostic\ntechniques to simplify the identification of cancer cells, which helps monitor the success of cancer\ntherapy. One of the most significant obstacles that chemotherapy must overcome is the absence of\ntechnologies that can measure its effectiveness while it is being administered. Additionally, due to its\nsteadily expanding prevalence and mortality rate, cancer has surpassed AIDS as the world's secondlargest\nkiller. Breast cancer accounts for a disproportionately high number of cancer-related deaths\namong women worldwide, making precise, sensitive imaging a necessity for this disease. When\nbreast cancer is diagnosed early it can be treated successfully. As an alternate strategy, the use of cutting-\nedge computational methodologies has been advocated for creating innovative breast cancer diagnostic\nimaging techniques. The following article provides an overview of the traditional diagnostic\nprocedures that have historically been employed for the detection of breast carcinoma, as well as the\ncurrent methods that are being utilized. Furthermore, the investigators provided a comprehensive\noverview of various mathematical frameworks, including Machine Learning, Deep Learning, Artificial\nNeural Networks, and Robotics, highlighting their progress and potential applications in the field\nof breast cancer diagnostic imaging.\n","PeriodicalId":503819,"journal":{"name":"Current Cancer Therapy Reviews","volume":"159 8‐10","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Cancer Therapy Reviews","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/0115733947288059240405041146","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Since breast cancer affects one in every four women, it is of the utmost need to investigate
novel diagnostic technologies and treatment techniques. This requires the development of diagnostic
techniques to simplify the identification of cancer cells, which helps monitor the success of cancer
therapy. One of the most significant obstacles that chemotherapy must overcome is the absence of
technologies that can measure its effectiveness while it is being administered. Additionally, due to its
steadily expanding prevalence and mortality rate, cancer has surpassed AIDS as the world's secondlargest
killer. Breast cancer accounts for a disproportionately high number of cancer-related deaths
among women worldwide, making precise, sensitive imaging a necessity for this disease. When
breast cancer is diagnosed early it can be treated successfully. As an alternate strategy, the use of cutting-
edge computational methodologies has been advocated for creating innovative breast cancer diagnostic
imaging techniques. The following article provides an overview of the traditional diagnostic
procedures that have historically been employed for the detection of breast carcinoma, as well as the
current methods that are being utilized. Furthermore, the investigators provided a comprehensive
overview of various mathematical frameworks, including Machine Learning, Deep Learning, Artificial
Neural Networks, and Robotics, highlighting their progress and potential applications in the field
of breast cancer diagnostic imaging.