乳腺癌是一种常见的恶性肿瘤，对其进行早期检测和治疗至关重要。基于深度学习的计算机辅助诊断（CAD）在医学诊断方面取得了显著的进展，近年来提高了准确性和效率。然而，尽管这项技术具有便利性，但也存在一定的局限性。当病理切片的形态特征不明显或复杂时，某些小病灶或病灶内部的深层细胞可能无法被识别，容易出现误诊。因此，本研究提出了一种基于卷积神经网络的多维特征融合网络（MDFF-Net）。该模型由一维特征提取网络、二维特征提取网络和特征融合分类网络组成。二维特征提取网络的基本部分由集成了多尺度通道混洗网络和通道注意力模块的模块堆叠而成。此外，受自然语言处理的启发，本模型还集成了一维特征提取网络，以提取图像中的详细信息，避免因细胞形态特征和分化程度等信息提取不足而导致误诊。最后，提取的一维和二维特征在特征融合网络中进行融合，并用于最终的分类。在BreakHis和BACH数据集上评估了MDFF-Net和经典分类模型的有效性。根据实验结果，MDFF-Net在BreakHis数据集上达到了98.86%的准确率，在BACH数据集上达到了86.25%的准确率。此外，为了进一步评估该模型在其他分类任务中的有效性，还使用结肠癌和肺癌数据集进行了额外的实验，在这两种情况下都实现了100%的分类准确率。版权所有©2023 Elsevier Ltd. 保留所有权利。

Breast cancer is a common malignancy and early detection and treatment of it is crucial. Computer-aided diagnosis (CAD) based on deep learning has significantly advanced medical diagnostics, enhancing accuracy and efficiency in recent years. Despite the convenience, this technology also has certain limitations. When the morphological characteristics of the patient's pathological section are not evident or complex, certain small lesions or cells deep within the lesion cannot be recognized, and misdiagnosis is prone to occur. As a result, MDFF-Net, a CNN-based multidimensional feature fusion network, is proposed. The model consists of a one-dimensional feature extraction network, a two-dimensional feature extraction network, and a feature fusion classification network. The basic part of the two-dimensional feature extraction network is stacked by modules integrated with multi-scale channel shuffling networks and channel attention modules. Furthermore, inspired by natural language processing, this model integrates a one-dimensional feature extraction network to extract detailed information in the image to avoid misdiagnosis caused by insufficient information extraction such as cell morphological characteristics and differentiation degree. Finally, the extracted one-dimensional and two-dimensional features are fused in the feature fusion network and employed for the final classification. The effectiveness of MDFF-Net and classical classification models were evaluated on the BreakHis and the BACH datasets. According to experimental results, MDFF-Net achieves an accuracy of 98.86% on the BreakHis and 86.25% on the BACH dataset. Furthermore, to further assess the effectiveness of the model in other classification tasks, the colon cancer and the lung cancer datasets were employed for additional experiments, achieving a classification accuracy of 100% in both cases.Copyright © 2023 Elsevier Ltd. All rights reserved.