Literature Review on Breast Cancer Diagnosis using 3D Images: Methods and Performance Analysis

Abstract

Breast cancer is becoming one of the leading causes of mortality in women all over the world. The advanced engineering of Artificial Intelligence techniques and natural image classification approaches is mostly employed to classify breast images. The image classification permits the physicians and the doctor the next chance, and this, in turn, saves the time of the physician and the doctor. Apart from several publications on classifying breast images, only some of the review papers are present that offer a clear definition of the “approaches to breast cancer image classification, feature extraction and selection methods, classification measurement parameterizations, and image classification results” with the help of 3D images. Machine learning (ML) seems to be an important portion of medical imaging research. These are developed over the years from manual seeded inputs to automatic initializations. The enhancements in the area of ML lead to various self-reliant and intelligent "Computer-Aided Diagnosis (CAD) systems" since the learning capability of the ML techniques is increasing in a constant manner. Several automated techniques are developing with deep feature representations and learning. The modern enhancements of ML with extensive and deeper representation techniques called Deep Learning (DL) techniques have created an important effect on enhancing the diagnosing ability of these systems. The main objective is to undergo a critical review on diverse contributions related to breast cancer detection that could be highly advantageous for dealing with 3D mammogram images. This paper plans to make a review on different algorithmic categorizations, datasets used, simulation platforms, and performance metrics. Furthermore, the research gaps and challenges of the breast cancer diagnosis using 3D images are elaborated, encouraging the upcoming researchers to give tremendous concern on new contributions.