Systematic Review of Computing Approaches for Breast Cancer Detection Based Computer Aided Diagnosis Using Mammogram Images
Abstract
ABSTRACT Breast cancer is one of the most prevalent types of cancer that plagues females. Mortality from breast cancer could be reduced by diagnosing and identifying it at an early stage. To detect breast cancer, various imaging modalities can be used, such as mammography. Computer-Aided Detection/Diagnosis (CAD) systems can assist an expert radiologist to diagnose breast cancer at an early stage. This paper introduces the findings of a systematic review that seeks to examine the state-of-the-art CAD systems for breast cancer detection. This review is based on 118 publications published in 2018–2021 and retrieved from major scientific publication databases while using a rigorous methodology of a systematic review. We provide a general description and analysis of existing CAD systems that use machine learning methods as well as their current state based on mammogram image modalities and classification methods. This systematic review presents all stages of CAD including pre-processing, segmentation, feature extraction, feature selection, and classification. We identify research gaps and outline recommendations for future research. This systematic review may be helpful for both clinicians, who use CAD systems for early diagnosis of breast cancer, as well as for researchers to find knowledge gaps and create more contributions for breast cancer diagnostics.
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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