Breast Cancer Screening Tool Using Gabor Filter-Based Ensemble Machine Learning Algorithms

Authors

  • P. Narasimhaiah Research Scholar. Department of CSE,YSR Engineering College of YVU, Proddatur - 516360, INDIA
  • C. Nagaraju Professor,Department of CSE,YSR Engineering College of YVU, Proddatur-516360, INDIA

Keywords:

Artefact, Cancer, Diagnostic, Gabor Filter, Radiologist, Random Forest

Abstract

The most common kind of cancer among females that causes death is breast cancer. It's early detection and initial treatment can save the patient's life and also decrease the mortality rate. An efficient approach to finding breast cancer at an initial stage is screening mammography. However the diagnostic procedure is hand-operated, time-taking, and a specialist radiology is required and available only in hospitals, so the patient cannot check at their home with this technology. In the literature, many techniques have existed, but fail to produce a high accuracy rate due to the presence of noise, artefacts, pectoral muscles, and low contrast. Based on these reasons it is difficult for radiologists to find cancer at the initial stage. This paper presents the Gabor filter-based ensemble machine learning technique which gives a high accuracy rate in the presence of noise, artefacts, pectoral muscles, and low contrast. This method is applied on all MIAS Datasets, which consist of 322 mammogram images and produce an accuracy of 98.98%.

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Published

17.02.2023

How to Cite

Narasimhaiah, P. ., & Nagaraju, C. . (2023). Breast Cancer Screening Tool Using Gabor Filter-Based Ensemble Machine Learning Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 11(2), 936–947. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2972

Issue

Vol. 11 No. 2 (2023)

Section

Research Article

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