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Lightweight Separable Convolution Network for Breast Cancer Histopathological Identification.

Grace Ugochi Nneji1,2, Happy Nkanta Monday1,2, Goodness Temofe Mgbejime3

  • 1Department of Computing, Oxford Brookes College of Chengdu University of Technology, Chengdu 610059, China.

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|January 21, 2023
PubMed
Summary
This summary is machine-generated.

A new lightweight separable convolution network (LWSC) effectively analyzes histopathological images for breast cancer detection. This automated system achieves high accuracy, aiding pathologists and potentially saving lives.

Keywords:
CNNbreast cancerdeep learninghistopathological imageimage identificationlightweight network

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Area of Science:

  • Medical Imaging
  • Computational Pathology
  • Artificial Intelligence in Oncology

Background:

  • Breast cancer is a leading global cause of female mortality.
  • Histopathological image analysis is crucial for diagnosis but is labor-intensive.
  • Automated systems are needed to assist pathologists and improve diagnostic efficiency.

Purpose of the Study:

  • To develop an automated system for breast cancer classification from histopathological images.
  • To introduce a novel lightweight separable convolution network (LWSC) for this task.
  • To address challenges of low image quality and reduce computational costs.

Main Methods:

  • Proposed a lightweight separable convolution network (LWSC) architecture.
  • Employed contrast enhancement for feature extraction from low-quality images.
  • Utilized parallel separable convolution layers with varied filter sizes for wider receptive fields.
  • Incorporated factorization and bottleneck layers to reduce model dimensions and computational cost.

Main Results:

  • The LWSC model achieved 97.23% accuracy, 97.71% sensitivity, and 97.93% specificity.
  • Demonstrated optimal performance on multi-class breast cancer categories.
  • Showcased comparable performance against existing models.

Conclusions:

  • The proposed LWSC model offers an efficient and effective automated solution for breast cancer classification.
  • LWSC demonstrates high accuracy and reduced computational requirements.
  • This approach can significantly support pathologists in diagnosing breast cancer from histopathological images.