Fang Weijian, Tang Shuyu, Yan Dongfang, Dai Xiangguang, Zhang Wei, Xiong Jiang
Chongqing Three Gorges University, Chongqing, China.
School of Computer Science and Engineering, School of Three Gorges Artificial Intelligence, and Key Laboratory of Intelligent Information Processing and Control, Chongqing Three Gorges University, Chongqing, China.
PLoS One. 2025 May 19;20(5):e0311728. doi: 10.1371/journal.pone.0311728. eCollection 2025.
This study presents a convolutional neural network (CNN)-based method for the classification and recognition of breast cancer pathology images. It aims to solve the problems existing in traditional pathological tissue analysis methods, such as time-consuming and labour-intensive, and possible misdiagnosis or missed diagnosis. Using the idea of ensemble learning, the image is divided into four equal parts and sixteen equal parts for data augmentation. Then, using the Inception-ResNet V2 neural network model and transfer learning technology, features are extracted from pathological images, and a three-layer fully connected neural network is constructed for feature classification. In the recognition process of pathological image categories, the network first recognises each sub-image, and then sums and averages the recognition results of each sub-image to finally obtain the classification result. The experiment uses the BreaKHis dataset, which is a breast cancer pathological image classification dataset. It contains 7,909 images from 82 patients and covers benign and malignant lesion types. We randomly select 80% of the data as the training set and 20% as the test set and compare them with the Inception-ResNet V2, ResNet101, DenseNet169, MobileNetV3 and EfficientNetV2 models. Experimental results show that under the four magnifications of the BreaKHis dataset, the method used in this study achieves the highest accuracy rates of 99.75%, 98.31%, 98.51% and 96.69%, which are much higher than other models.
本研究提出了一种基于卷积神经网络（CNN）的乳腺癌病理图像分类与识别方法。其旨在解决传统病理组织分析方法中存在的问题，如耗时费力，以及可能出现的误诊或漏诊。利用集成学习的思想，将图像划分为四个相等部分和十六个相等部分进行数据增强。然后，使用Inception-ResNet V2神经网络模型和迁移学习技术，从病理图像中提取特征，并构建一个三层全连接神经网络进行特征分类。在病理图像类别的识别过程中，网络首先识别每个子图像，然后对每个子图像的识别结果进行求和与平均，最终得到分类结果。实验使用BreaKHis数据集，这是一个乳腺癌病理图像分类数据集。它包含来自82名患者的7909张图像，涵盖良性和恶性病变类型。我们随机选择80%的数据作为训练集，20%作为测试集，并与Inception-ResNet V2、ResNet101、DenseNet169、MobileNetV3和EfficientNetV2模型进行比较。实验结果表明，在BreaKHis数据集的四种放大倍数下，本研究使用的方法分别达到了99.75%、98.31%、98.51%和96.69%的最高准确率，远高于其他模型。
