Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example

TL;DR

This paper presents a CNN-based method using transfer learning and image partitioning to automatically classify breast cancer pathological images into benign or malignant, achieving over 92% accuracy on a public dataset.

Contribution

It introduces a novel CNN approach with image partitioning and probability aggregation algorithms for rapid, accurate breast cancer image classification.

Findings

Accuracy surpassing 92% across all magnifications

Effective use of Inceptionv3 and transfer learning for feature extraction

Improved classification efficiency and accuracy

Abstract

Breast cancer is a relatively common cancer among gynecological cancers. Its diagnosis often relies on the pathology of cells in the lesion. The pathological diagnosis of breast cancer not only requires professionals and time, but also sometimes involves subjective judgment. To address the challenges of dependence on pathologists expertise and the time-consuming nature of achieving accurate breast pathological image classification, this paper introduces an approach utilizing convolutional neural networks (CNNs) for the rapid categorization of pathological images, aiming to enhance the efficiency of breast pathological image detection. And the approach enables the rapid and automatic classification of pathological images into benign and malignant groups. The methodology involves utilizing a convolutional neural network (CNN) model leveraging the Inceptionv3 architecture and transfer learning algorithm for extracting features from pathological images. Utilizing a neural network with fully connected layers and employing the SoftMax function for image classification. Additionally, the concept of image partitioning is introduced to handle high-resolution images. To achieve the ultimate classification outcome, the classification probabilities of each image block are aggregated using three algorithms: summation, product, and maximum. Experimental validation was conducted on the BreaKHis public dataset, resulting in accuracy rates surpassing 0.92 across all four magnification coefficients (40X, 100X, 200X, and 400X). It demonstrates that the proposed method effectively enhances the accuracy in classifying pathological images of breast cancer.