Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction

Abstract

Breast cancer is one of the precarious conditions that affect women, and a substantive cure has not yet been discovered for it. With the advent of Artificial intelligence (AI), recently, deep learning techniques have been used effectively in breast cancer detection, facilitating early diagnosis and therefore increasing the chances of patients’ survival. Compared to classical machine learning techniques, deep learning requires less human intervention for similar feature extraction. This study presents a systematic literature review on the deep learning-based methods for breast cancer detection that can guide practitioners and researchers in understanding the challenges and new trends in the field. Particularly, different deep learning-based methods for breast cancer detection are investigated, focusing on the genomics and histopathological imaging data. The study specifically adopts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), which offer a detailed analysis and synthesis of the published articles. Several studies were searched and gathered, and after the eligibility screening and quality evaluation, 98 articles were identified. The results of the review indicated that the Convolutional Neural Network (CNN) is the most accurate and extensively used model for breast cancer detection, and the accuracy metrics are the most popular method used for performance evaluation. Moreover, datasets utilized for breast cancer detection and the evaluation metrics are also studied. Finally, the challenges and future research direction in breast cancer detection based on deep learning models are also investigated to help researchers and practitioners acquire in-depth knowledge of and insight into the area.

Artificial intelligence in breast imaging: potentials and challenges

“…As a subset of DL and the most common type of neural network, the CNN is suited to identify particular patterns in images which can occur at different locations because the convolution operation is spatially invariant (Burt et al 2018, Robertson et al 2018. In a systemic review, Nasser and Yusof found that CNN model has the most accurate performance with the most extensive application for breast cancer diagnosis (Nasser and Yusof 2023). A CNN consists of three layers: an input layer, a hidden layer (one or more hidden convolutional layers), and an output layer (Pesapane et al 2018).…”

A Framework for Interpretability in Machine Learning for Medical Imaging

“…CASE STUDY 2: BREAST CANCER DETECTION 1) Problems and goals. Early detection of breast cancer is important for increasing the chances of patients' survival [144]. A large part of clinical diagnosis in this area is concerned with characterizing various features of the tumor from mammograms, including masses, calcification, architectural distortion, asymmetries, and other related signs [145].…”

The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide

“…Several studies have reviewed the application of deep learning in cancer research, highlighting its potential in advancing cancer diagnosis, prognosis, and treatment prediction. 57 , 58 …”