###### Abstract  
Addressing the challenge of achieving precise subtype classification of breast cancer histopathology images with limited resources, a lightweight model incorporating multi-stage information fusion and an attention mechanism is proposed for this task. Using MobileNetV3 as the backbone, a multi-stage fusion strategy captures the rich image information in breast cancer histopathology images. Additionally, the selective kernel (SK) attention mechanism is introduced in the initial stages of feature extraction, while an improved squeeze-and-excitation coordinate attention (SCA) mechanism is integrated in the later stages to enhance the extraction of both underlying and semantic features. The final feature representations for subtype classification are determined based  
achieving an accuracy of 96.19% for classifying breast cancer images as benign or malignant. However, with complex and diverse pathological tissue images, traditional feature descriptors often capture only shallow features and cannot comprehensively and accurately represent deep information and complex structures. Advancements in technology have led to the integration of deep learning methods into medical image analysis, providing notable benefits. Spanhol et al. [11] utilized the AlexNet model as the core network to classify benign and malignant breast cancer pathology images from the BreakHis dataset, employing four different strategies for image block fusion, resulting in a 6% accuracy improvement over traditional machine learning methods. Gour et al. [12] developed a ResHist model based on residual learning, combined with data enhancement techniques, achieving an accuracy of 92.52% in classifying benign and malignant images from the BreakHis dataset. Miaolin Zhang et al. [13] introduced a DC-DenseNet model with DenseNet as the backbone network, incorporating dilation convolution in dense blocks to capture multi-scale features, reaching an accuracy of 94.10% on the BACH dataset for four-class classification. Kausar et al. [14] employed the Haar wavelet transform to decompose breast cancer images, reducing the computational load and resource usage of the deep convolutional neural network (CNN) during convolution. By merging features from each level of the VGG16 model for the multi-classification of breast cancer images in the BreakHis dataset, they achieved an accuracy of 96.85%. Srikantamurthy et al. [15] combined a CNN with a long- and short-term memory recurrent neural network to create a hybrid model, attaining different magnification accuracies of 96.5%, 92.6%, 88.94%, and 92.51% for breast cancer images from the BreakHis dataset. The multi-layer neural network architecture utilizing convolutional neural networks (CNNs) effectively captures the deep semantic features of images, significantly enhancing the accuracy and reliability of image recognition. Despite these advancements, deep learning faces several challenges and limitations in medical image classification. Firstly, deep learning models require substantial labeled training data, which are often scarce and unevenly distributed in medical imaging. Additionally, these models have complex structures with numerous parameters, leading to long training times and high computational resource demands, thus hindering their deployment and practical application in computer-aided diagnosis. To address these issues and enhance the accuracy of breast cancer histopathological image subtype classification while maintaining a lightweight design, this paper proposes a network model incorporating multi-layer information fusion and an attention mechanism. This approach enhances classification accuracy and ensures model efficiency, facilitating accurate breast cancer subtype classification and supporting doctors in making quick and precise diagnoses. This article is organized as follows: Section 2 presents the overall model design.