A systematic survey of deep learning in breast cancer

Abstract

In recent years, we witnessed a speeding development of deep learning in computer vision fields like categorization, detection, and semantic segmentation. Within several years after the emergence of AlexNet, the performance of deep neural networks has already surpassed human being experts in certain areas and showed great potential in applications such as medical image analysis. The development of automated breast cancer detection systems that integrate deep learning has received wide attention from the community. Breast cancer, a major killer of females that results in millions of deaths, can be controlled even be cured given that it is detected at an early stage with sophisticated systems. In this paper, we reviewed breast cancer diagnosis, detection, and segmentation computer-aided (CAD) systems based on state-of-the-art deep convolutional neural networks. The available data sets also indirectly determine CAD systems' performance, so we introduced and discussed the details of public data sets. The challenges remaining in CAD systems for breast cancer are discussed at the end of this paper. The highlights of this survey mainly come from three following aspects. First, we covered a wide range of the basics of breast cancer from imaging modalities to popular databases in the community; Second, we presented the key elements in deep learning to form the compactness for methods mentioned in reviewed papers; Third and lastly, the summative details in each reviewed paper are provided so that interested readers can have a refined version of these works without referring to original papers.Therefore, this systematic survey suits readers with varied backgrounds and will be beneficial to them.

Design and Implementation of Chinese Common Braille Translation System Integrating Braille Word Segmentation and Concatenation Rules

“…In recent years, neural networks and deep learning methods are very eye-catching techniques [ 18 ]. Deep learning has made outstanding achievements in natural language processing, image recognition (especially medical image recognition [ 19 , 20 ]), target detection, and so on. Deep learning includes Convolutional Neural Networks (CNNs) to Recurrent Neural Networks (RNNs) to long- and short-term memory neural network model LSTM [ 21 ] and improved variations of LSTM.…”

Applying Deep Learning for Breast Cancer Detection in Radiology

“…The main contributions of this review are as follows: Our paper provides a detailed review on the available breast cancer datasets of each of the 4 different modalities not available in past reviews [ 16 , 17 , 18 ]. We explore the methods in which recent deep learning algorithms are used to detect breast cancer using different types of screening.…”

Classification of breast cancer using a manta-ray foraging optimized transfer learning framework

“…With approximately 26% of women affected worldwide, it is regarded as the first cancer type to cause death. However, detecting and diagnosing breast cancer in its early stages can enhance survival rates by up to 80% ( Yu et al, 2022 ). Clinicians must identify suspicious tumors after segmenting them to diagnose breast abnormalities.…”

“…In addition, this approach cuts down the number of false positives caused by human mistakes. Several researches that discuss the use of CAD systems to diagnose breast cancer have been published in the literature, some of which have already passed clinical testing ( Yu et al, 2022 ; Bruno et al, 2020 ). This study introduces a framework for reliable breast cancer classification based on histopathological and ultrasound data using CNN and Transfer Learning (TL) ( Agarwal et al, 2021 ).…”