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Breast cancer pathological image classification based on deep learning.

Yubao Hou1

  • 1School of Information and Mechanical Engineering, Hunan International Economics University, Changsha, China.

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Summary

This study presents an improved deep learning model for automatic breast cancer classification from pathological images. The model achieves 91% accuracy, offering a robust and generalizable solution for clinical applications.

Keywords:
Breast cancer histopathological image classificationconvolutional neural networkdata augmentationdeep leaningopen dataset of BreaKHistransfer learning

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Area of Science:

  • Pathology
  • Computer Science
  • Artificial Intelligence

Background:

  • Accurate breast cancer classification from pathological images is crucial for clinical decision-making.
  • Traditional methods relying on manual feature extraction are complex, time-consuming, and require specialized expertise.
  • Deep learning offers a promising alternative for automated image analysis, but faces challenges with limited data.

Purpose of the Study:

  • To develop and apply an improved deep convolutional neural network (CNN) for automated breast cancer pathological image classification.
  • To address challenges associated with manual feature extraction and deep learning model limitations.

Main Methods:

  • An improved deep convolutional neural network (CNN) model was developed.
  • Data augmentation and transfer learning techniques were employed to mitigate overfitting and enhance model performance with limited training data.
  • The model was evaluated on the publicly available BreaKHis dataset.

Main Results:

  • The improved deep learning model achieved a recognition accuracy of 91% on the BreaKHis dataset.
  • The model demonstrated good robustness and generalization capabilities when compared to previous approaches.
  • The developed method effectively overcomes limitations of manual feature extraction.

Conclusions:

  • The proposed improved deep CNN model provides an effective and automated approach for breast cancer pathological image classification.
  • The integration of data augmentation and transfer learning enhances model performance and generalizability, addressing overfitting issues.
  • This automated classification method holds significant clinical application value, potentially improving diagnostic efficiency and accuracy.