Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Abstract

Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.

Artificial Intelligence Predicted Overall Survival and Classified Mature B-Cell Neoplasms Based on Immuno-Oncology and Immune Checkpoint Panels

“…The development of strategies for the early detection of breast cancer is necessary to improve access to treatment and reduce the mortality rate. As described by Basurto-Hurtado JA et al [ 90 ], breast cancer detection includes four steps: image acquisition, segmentation and pre-processing, feature extraction, and classification [ 90 ]. Image acquisition can be obtained through several methods, such as mammography, ultrasound, magnetic resonance imaging (MRI), and other approaches, including microwave, computed tomography (TC), and positron emission tomography (PET) [ 90 ].…”

“…As described by Basurto-Hurtado JA et al [ 90 ], breast cancer detection includes four steps: image acquisition, segmentation and pre-processing, feature extraction, and classification [ 90 ]. Image acquisition can be obtained through several methods, such as mammography, ultrasound, magnetic resonance imaging (MRI), and other approaches, including microwave, computed tomography (TC), and positron emission tomography (PET) [ 90 ]. The image processing and classification strategies include several steps: region of interest (ROI) estimation, and feature extraction.…”

“…Examples of unsupervised classifiers include K-means and hierarchical clustering. Examples of supervised classifiers are decision trees, random forests, AdaBoost, support vector machines, artificial neural networks, and convolutional neural networks [ 90 ]. Recently, new image generation techniques have developed, such as infrared thermography (IRT).…”

“…This technique has been successfully applied to breast cancer; the classification methods included several machine learning and artificial neural networks, and the accuracy ranged from 90% to 100% [ 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 ]. Recently, new classification algorithms have been developed, including autoencoders, deep belief networks, ladder networks, and deep neural network (DNN)-based algorithms such as the deep Kronecker neural network [ 90 , 102 ]. Gene expression profiling is a useful tool in medical research, both for diagnosis and for the elucidation of the disease pathogenesis.…”

“…The development of strategies for the early detection of breast cancer is necessary to improve access to treatment and reduce the mortality rate. As described by Basurto-Hurtado JA et al [ 90 ], breast cancer detection includes four steps: image acquisition, segmentation and pre-processing, feature extraction, and classification [ 90 ].…”