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Related Concept Videos

Parallel Processing01:20

Parallel Processing

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Convolutional neural network with parallel convolution scale attention module and ResCBAM for breast histology image

Ting Yan1, Guohui Chen1, Huimin Zhang2

  • 1Translational Medicine Research Center, Shanxi Medical University, Taiyuan, China.

Heliyon
|May 21, 2024
PubMed
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A new lightweight deep learning model, PCSAM-ResCBAM, improves early breast cancer detection from pathological images. This automated system enhances diagnostic accuracy, aiding clinicians in patient treatment and prognosis.

Keywords:
Breast cancerDilation convolutionFeature fusionParallel convolution scale attentionResCBAM

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Area of Science:

  • Medical Imaging
  • Computational Pathology
  • Artificial Intelligence in Oncology

Background:

  • Breast cancer is a leading cause of female mortality globally, necessitating early detection for improved patient outcomes.
  • Accurate pathological image classification is crucial for computer-aided diagnosis and prognosis in breast cancer.
  • Existing automated methods face challenges with scale information, feature fusion, and model complexity, impacting classification accuracy and efficiency.

Purpose of the Study:

  • To develop a lightweight, two-stage convolutional neural network model for accurate and efficient automatic detection and classification of breast cancer from pathological images.
  • To address limitations in scale information handling and feature fusion in current deep learning models for breast cancer pathology.

Main Methods:

  • Proposed a novel lightweight PCSAM-ResCBAM model, incorporating a Parallel Convolution Scale Attention Module network (PCSAM-Net) and a Residual Convolutional Block Attention Module network (ResCBAM-Net).
  • Employed a two-stage approach: a 4-layer PCSAM module for patch-level classification and a second-level network utilizing tiled feature fusion and residual attention for image-level classification.
  • Utilized the ICIAR2018 and BreakHis datasets for model evaluation.

Main Results:

  • The PCSAM-ResCBAM model achieved a maximum accuracy of 98.74% on 200× and 400× magnification datasets.
  • Ablation studies confirmed the significant contribution of scale attention and dilated convolution to model performance.
  • The proposed model demonstrated superior performance compared to existing state-of-the-art methods.

Conclusions:

  • The lightweight PCSAM-ResCBAM model offers a significant advancement in automated breast cancer pathological image classification.
  • The model's architecture effectively handles scale variations and integrates features, leading to enhanced diagnostic accuracy.
  • This system holds great potential for assisting clinical diagnosis and improving breast cancer patient prognosis through early and accurate detection.