A fully-convolutional residual encoder-decoder neural network to localize breast cancer on histopathology images

Nacer Farajzadeh; Nima Sadeghzadeh; Mahdi Hashemzadeh

doi:10.1016/j.compbiomed.2022.105698

A fully-convolutional residual encoder-decoder neural network to localize breast cancer on histopathology images

Comput Biol Med. 2022 Aug:147:105698. doi: 10.1016/j.compbiomed.2022.105698. Epub 2022 Jun 9.

Authors

Nacer Farajzadeh¹, Nima Sadeghzadeh², Mahdi Hashemzadeh³

Affiliations

¹ Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran; Artificial Intelligence and Machine Learning Research Laboratory, Azarbaijan Shahid Madani University, Tabriz, Iran. Electronic address: n.farajzadeh@azaruniv.ac.ir.
² Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran; Artificial Intelligence and Machine Learning Research Laboratory, Azarbaijan Shahid Madani University, Tabriz, Iran. Electronic address: nima.sa@icloud.com.
³ Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran; Artificial Intelligence and Machine Learning Research Laboratory, Azarbaijan Shahid Madani University, Tabriz, Iran. Electronic address: hashemzadeh@azaruniv.ac.ir.

PMID: 35714505
DOI: 10.1016/j.compbiomed.2022.105698

Abstract

Cancer detection in its early stages may allow patients to receive the proper treatment and save lives along with recovering the routine lifestyles. Breast cancer is of the top leading causes of mortality among women all around the globe. A source to find these cancerous nuclei is through analyzing histopathology images. These images, however, are very complex and large. Thus, locating the cancerous nuclei in them is very challenging. Hence, if an expert fails to diagnose their patients via these images, the situation may be exacerbated. Therefore, this study aims to introduce a method to mask as many cancer nuclei on histopathology images as possible with a high visual aesthetic to make them distinguishable by experts easily. A tailored residual fully convolutional encoder-decoder neural network based on end-to-end learning is proposed to issue the matter. The proposed method is evaluated quantitatively and qualitatively on ER + BCa H&E-stained dataset. The average detection accuracy achieved by the method is 98.61%, which is much better than that of competitors.

Keywords: Breast cancer nuclei; Encoder-decoder; End-to-End learning; Fully convolutional neural networks; Image masking; Residual networks.

MeSH terms

Breast Neoplasms* / diagnostic imaging
Breast Neoplasms* / pathology
Cell Nucleus / pathology
Female
Humans
Image Processing, Computer-Assisted / methods
Neural Networks, Computer