Next Article in Journal
SHOX Deletion and Idiopathic Short Stature: What Does the Clinician Need to Know? Case Series Report
Previous Article in Journal
Adaptive IoU Thresholding for Improving Small Object Detection: A Proof-of-Concept Study of Hand Erosions Classification of Patients with Rheumatic Arthritis on X-ray Images
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Diagnostics
Volume 13
Issue 1
10.3390/diagnostics13010103
diagnostics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Efficient Breast Cancer Classification Network with Dual Squeeze and Excitation in Histopathological Images

by
Md. Mostafa Kamal Sarker
1,2,*,†,
Farhan Akram
3,†,
Mohammad Alsharid
2,4,
Vivek Kumar Singh
5,
Robail Yasrab
2 and
Eyad Elyan
6
1
National Subsea Center, Robert Gordon University, Aberdeen AB10 7AQ, UK
2
Institute of Biomedical Engineering, University of Oxford, Oxford OX1 2JD, UK
3
Department of Pathology and Clinical Bioinformatics, Erasmus Medical Center, 3015 GD Rotterdam, The Netherlands
4
Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates
5
Independent Researcher, Belfast BT9 5GD, UK
6
School of Computing Science and Digital Media, Robert Gordon University, Aberdeen AB10 7AQ, UK
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Diagnostics 2023, 13(1), 103; https://doi.org/10.3390/diagnostics13010103
Submission received: 8 November 2022 / Revised: 13 December 2022 / Accepted: 20 December 2022 / Published: 29 December 2022
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
Browse Figures
Versions Notes

Abstract

Medical image analysis methods for mammograms, ultrasound, and magnetic resonance imaging (MRI) cannot provide the underline features on the cellular level to understand the cancer microenvironment which makes them unsuitable for breast cancer subtype classification study. In this paper, we propose a convolutional neural network (CNN)-based breast cancer classification method for hematoxylin and eosin (H&E) whole slide images (WSIs). The proposed method incorporates fused mobile inverted bottleneck convolutions (FMB-Conv) and mobile inverted bottleneck convolutions (MBConv) with a dual squeeze and excitation (DSE) network to accurately classify breast cancer tissue into binary (benign and malignant) and eight subtypes using histopathology images. For that, a pre-trained EfficientNetV2 network is used as a backbone with a modified DSE block that combines the spatial and channel-wise squeeze and excitation layers to highlight important low-level and high-level abstract features. Our method outperformed ResNet101, InceptionResNetV2, and EfficientNetV2 networks on the publicly available BreakHis dataset for the binary and multi-class breast cancer classification in terms of precision, recall, and F1-score on multiple magnification levels.
Keywords: breast cancer; histopathology; convolutional neural network; dual squeeze; excitation mechanism breast cancer; histopathology; convolutional neural network; dual squeeze; excitation mechanism

Share and Cite

MDPI and ACS Style

Sarker, M.M.K.; Akram, F.; Alsharid, M.; Singh, V.K.; Yasrab, R.; Elyan, E. Efficient Breast Cancer Classification Network with Dual Squeeze and Excitation in Histopathological Images. Diagnostics 2023, 13, 103. https://doi.org/10.3390/diagnostics13010103

AMA Style

Sarker MMK, Akram F, Alsharid M, Singh VK, Yasrab R, Elyan E. Efficient Breast Cancer Classification Network with Dual Squeeze and Excitation in Histopathological Images. Diagnostics. 2023; 13(1):103. https://doi.org/10.3390/diagnostics13010103

Chicago/Turabian Style

Sarker, Md. Mostafa Kamal, Farhan Akram, Mohammad Alsharid, Vivek Kumar Singh, Robail Yasrab, and Eyad Elyan. 2023. "Efficient Breast Cancer Classification Network with Dual Squeeze and Excitation in Histopathological Images" Diagnostics 13, no. 1: 103. https://doi.org/10.3390/diagnostics13010103

APA Style

Sarker, M. M. K., Akram, F., Alsharid, M., Singh, V. K., Yasrab, R., & Elyan, E. (2023). Efficient Breast Cancer Classification Network with Dual Squeeze and Excitation in Histopathological Images. Diagnostics, 13(1), 103. https://doi.org/10.3390/diagnostics13010103

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop