## 1 Introduction  
Breast cancer remains one of the most prevalent cancers worldwide. In 2022, it accounted for over 2.3 million new cases, ranking second among all cancer types globally. Moreover, with about 666,000 deaths, breast cancer ranks as the fourth most frequent contributor to cancer mortality worldwide. A timely and precise diagnosis of breast cancer can prolong the lives of patients or increase their chance of survival [1]. However, the accuracy of pathological diagnosis relies not only on image texture quality but also on subjective elements like the pathologist's focus and level of experience. Consequently, the risk of misdiagnosis remains, which may negatively influence patient prognosis and clinical outcomes. In order to achieve more accurate and efficient breast cancer pathology assessments, leveraging advanced computational techniques to assist pathologists in pathological tissue analysis holds significant clinical value [2]. Convolutional neural networks (CNNs) have demonstrated outstanding performance in medical image processing tasks [3, 4, 5, 6, 7]. CNN-related breast cancer histopathological image (BCHI) classification methods have also made considerable progress [8; 9; 10; 11; 12; 13; 14]. Currently, CNN-based approaches for BCHI classification can be broadly divided into two categories. Non-end-to-end training model can be used to describe the first category. Prior to using machine learning classifiers to categorize samples based on the extracted features, researchers first extract features from input images using conventional or newly developed CNNs. In this process, researchers typically pre-train CNN models using the ImageNet dataset [15]. For example, [PERSON] et al. [16] used the VGG-16 model pre-trained on ImageNet to extract deep features from BCHI and evaluate classification performance by combining support vector machines and random forest methods. The second stream of classification for BCHI use end-to-end training of the deep neural network model. This stream prevents the multi-module network model's discontinuity between the stage of feature extraction and the classifier, which is further divided into two subcategories. The first subtype is task-specific CNN approaches, which involve fine-tuning traditional pre-trained networks for certain tasks [10; 11]. For example, in the work of [PERSON] and [PERSON] [10], commonly adopted pre-trained models including VGG16, VGG19, and ResNet50 were evaluated against fully trained counterparts on BCHI. [PERSON] et al. [11] incorporated global and local characteristics on six standard VGGNet and ResNet models, and examined the models' ability to classify BCHI systematically. ResNet50 outperforms the other six traditional network models, according to experimental findings. [PERSON] et al. [17] performed BCHI classification by fine-tuning Inception-V3 convolutional neural network pre-trained on ImageNet. [PERSON] et al. [18] utilized a ResNet18 model pre-trained on ImageNet to fine-tune its terminal residual modules. They also combine global contrast normalization and data augmentation, achieving significant performance advantages on the BreakHis dataset. Another subclass is to add representative convolutional neural network units to the classical network or the newly constructed network. [PERSON] et al. [19] proposed a seven-layer BCHI classification network, with two fully connected layers placed after five convolutional stages. Through data enhancement technology, the accuracy of the two models on the BreakHis dataset was between 96.15% and 98.33%. In order to extract picture features and address the multi-classification problem with fewer computational resources, [PERSON] et al. [20] suggested a tree-based breast tumor model. Studies have revealed that a single network with a straightforward structure is also capable of achieving great classification accuracy for this medical purpose.