Breast cancer pathology image recognition based on convolutional neural network

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.