Lightweight Separable Convolution Network for Breast Cancer Histopathological Identification

Breast cancer is one of the leading causes of death among women worldwide. Histopathological images have proven to be a reliable way to find out if someone has breast cancer over time, however, it could be time consuming and require much resources when observed physically. In order to lessen the bur...

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Bibliographic Details
Published in:Diagnostics (Basel) 2023-01, Vol.13 (2), p.299
Main Authors: Nneji, Grace Ugochi, Monday, Happy Nkanta, Mgbejime, Goodness Temofe, Pathapati, Venkat Subramanyam R, Nahar, Saifun, Ukwuoma, Chiagoziem Chima
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Breast cancer
Classification
CNN
Datasets
Deep learning
Discriminant analysis
histopathological image
Histopathology
image identification
lightweight network
Literature reviews
Mammography
Medical diagnosis
Medical screening
Neural networks
Performance evaluation
Risk assessment
Support vector machines
Tumors
Womens health
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Summary:Breast cancer is one of the leading causes of death among women worldwide. Histopathological images have proven to be a reliable way to find out if someone has breast cancer over time, however, it could be time consuming and require much resources when observed physically. In order to lessen the burden on the pathologists and save lives, there is need for an automated system to effectively analysis and predict the disease diagnostic. In this paper, a lightweight separable convolution network (LWSC) is proposed to automatically learn and classify breast cancer from histopathological images. The proposed architecture aims to treat the problem of low quality by extracting the visual trainable features of the histopathological image using a contrast enhancement algorithm. LWSC model implements separable convolution layers stacked in parallel with multiple filters of different sizes in order to obtain wider receptive fields. Additionally, the factorization and the utilization of bottleneck convolution layers to reduce model dimension were introduced. These methods reduce the number of trainable parameters as well as the computational cost sufficiently with greater non-linear expressive capacity than plain convolutional networks. The evaluation results depict that the proposed LWSC model performs optimally, obtaining 97.23% accuracy, 97.71% sensitivity, and 97.93% specificity on multi-class categories. Compared with other models, the proposed LWSC obtains comparable performance.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics13020299