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Principal Investigator
Arthur Rahming
Philander Smith University
Position Title
Student Researcher
About this CDAS Project
PLCO (Learn more about this study)
Project ID
Initial CDAS Request Approval
Feb 14, 2024
Mobile Based Breast Cancer Diagnosing System Using a Rough Set-Ensemble Classifier Approach
Breast cancer is a dominant factor of a life-threatening disease among females all over the world. In China during
the year 2018, 367, 900 new cases of Breast cancer were detected among the females according to International
Agency for Research on Cancer (IARC). Also, in the same year about 266,120 United States women were found
to have invasive breast cancer and about 40,920 of these women were estimated to have died [1]. In the last
decades based on the medical practitioners’ experience, it has been confirmed, that early detection and precis
prediction have been so helpful in ensuring a long survival of the breast cancer patients and reduce death rate.
Therefore, in ensuring early detection and accurate diagnosis of breast cancer different approaches have been
introduced in literature which include the Artificial intelligence approaches using machine learning algorithms. To
bridge this, gap, this project aims to develop a Mobile Based Breast Cancer Diagnosing System and Heath
assistance using a Rough Set-Ensemble Classifier approach. The scientific rationale for our central hypothesis
that Rough Set-Ensemble Classifier approach leverages powerful deep learning tools can help to improve cancer

-The overarching goal of this work is to develop a mobile based breast cancer diagnosing system using a Rough Set-Ensemble classifier approach.This work could be the foundation for a software product that canaccept breast
imaging data to improve breast cancer diagnose.
-Objective #1: Take advantage of different feature reductions by rough set reduct algorithms in building
different base learners models for more enhanced breast cancer predictive system.
-Objective #2: To advance personalized medicine, we will enhance precision of diagnosis, assessment of prognosis, estimation of survival, and prediction of treatment response.
-Objective#3: To infer the disease molecular background,we will use non-invasive correlated imaging features. These associations are lacking in the currently existing approaches.


Dr.Suzan Anwar, Syed Shah, Otito Udedibor, Mikea Fernander. All faculty and students from Philander Smith University