Deep learning approaches for breast cancer detection in histopathology images: A review

Abstract

Background: Breast cancer is one of the leading causes of death in women worldwide. Histopathology analysis of breast tissue is an essential tool for diagnosing and staging breast cancer. In recent years, there has been a significant increase in research exploring the use of deep-learning approaches for breast cancer detection from histopathology images.

Objective: To provide an overview of the current state-of-the-art technologies in automated breast cancer detection in histopathology images using deep learning techniques.

Methods: This review focuses on the use of deep learning algorithms for the detection and classification of breast cancer from histopathology images. We provide an overview of publicly available histopathology image datasets for breast cancer detection. We also highlight the strengths and weaknesses of these architectures and their performance on different histopathology image datasets. Finally, we discuss the challenges associated with using deep learning techniques for breast cancer detection, including the need for large and diverse datasets and the interpretability of deep learning models.

Results: Deep learning techniques have shown great promise in accurately detecting and classifying breast cancer from histopathology images. Although the accuracy levels vary depending on the specific data set, image pre-processing techniques, and deep learning architecture used, these results highlight the potential of deep learning algorithms in improving the accuracy and efficiency of breast cancer detection from histopathology images.

Conclusion: This review has presented a thorough account of the current state-of-the-art techniques for detecting breast cancer using histopathology images. The integration of machine learning and deep learning algorithms has demonstrated promising results in accurately identifying breast cancer from histopathology images. The insights gathered from this review can act as a valuable reference for researchers in this field who are developing diagnostic strategies using histopathology images. Overall, the objective of this review is to spark interest among scholars in this complex field and acquaint them with cutting-edge technologies in breast cancer detection using histopathology images.

Keywords: Computer-aided detection; Convolutional Neural Network (CNN); breast cancer; deep learning; histopathology images.

Figure 1.

Samples of breast histopathology images acquired from BreakHis data set, illustrated in different magnification factors [76]. (a) 40X, (b) 100X, (c) 200X, and (d) 400X.

Figure 2.

Sample images from BACH dataset [7] showing (a) normal, (b) benign, (c) in-situ, and (d) invasive categories.

Figure 3.

Overview of various image processing techniques used in the CAD of breast cancer detection.

Figure 4.

A general block schematic of various steps employed during computer-aided diagnosis in medical images.

Figure 5.

Illustration of artificial neural network (ANN).

Figure 6.

Illustration of basic blocks in a convolutional neural network.

Figure 7.

Schematic of transfer learning process.

Figure 8.

The basic structure of an autoencoder network.

Figure 9.

The concept of generative adversarial network (GAN).

Figure 10.

Schematic of a confusion matrix showing true positive (P1), true negative (N2), false positive (P2), and false negative (N1) cases.

Figure 11.

A sample ROC curve that visualizes the effectiveness of a binary classification model across different classification thresholds.