Classification of Hematoxylin and Eosin-Stained Breast Cancer Histology Microscopy Images Using Transfer Learning with EfficientNets.

Chanaleä Munien,Serestina Viriri,Vahid Rakhshan

doi:10.1155/2021/5580914

Chanaleä Munien, Serestina Viriri + Show 1 more

Open Access

https://doi.org/10.1155/2021/5580914

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Abstract

Breast cancer is a fatal disease and is a leading cause of death in women worldwide. The process of diagnosis based on biopsy tissue is nontrivial, time-consuming, and prone to human error, and there may be conflict about the final diagnosis due to interobserver variability. Computer-aided diagnosis systems have been designed and implemented to combat these issues. These systems contribute significantly to increasing the efficiency and accuracy and reducing the cost of diagnosis. Moreover, these systems must perform better so that their determined diagnosis can be more reliable. This research investigates the application of the EfficientNet architecture for the classification of hematoxylin and eosin-stained breast cancer histology images provided by the ICIAR2018 dataset. Specifically, seven EfficientNets were fine-tuned and evaluated on their ability to classify images into four classes: normal, benign, in situ carcinoma, and invasive carcinoma. Moreover, two standard stain normalization techniques, Reinhard and Macenko, were observed to measure the impact of stain normalization on performance. The outcome of this approach reveals that the EfficientNet-B2 model yielded an accuracy and sensitivity of 98.33% using Reinhard stain normalization method on the training images and an accuracy and sensitivity of 96.67% using the Macenko stain normalization method. These satisfactory results indicate that transferring generic features from natural images to medical images through fine-tuning on EfficientNets can achieve satisfactory results.

Highlights

Introduction and BackgroundOne of the leading causes of death in women throughout the world is breast cancer [1]
One could hypothesize that an increase in parameter count translates to a decrease in accuracy of this dataset. is indicates that the bigger architectures may have more difficulty extracting critical features from training images, even if measures are taken to enlarge the dataset being used. e results of this study emphasize the benefit of incorporating stain normalization into preprocessing and how choosing the correct method improves accuracy significantly
Evaluation Criteria. e performance of each model was evaluated by calculating the precision, recall, F1-score, and accuracy. e equations below represent the manner in which these metrics are determined

Summary

Introduction

One of the leading causes of death in women throughout the world is breast cancer [1]. It is defined as a group of diseases in which cells within the tissue of the breast alter and divide in an uncontrolled manner, generally resulting in lumps or growths. E paraffin blocks are sliced and fixed on glass slides. Interesting structures such as the cytoplasm and nuclei in the tissue are not yet apparent at this point. When added to the tissue, the hematoxylin can bind itself to deoxyribonucleic acid, which results in the nuclei in the tissue being dyed a blue/purple color.

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