Nneji Grace Ugochi, Monday Happy Nkanta, Mgbejime Goodness Temofe, Pathapati Venkat Subramanyam R, Nahar Saifun, Ukwuoma Chiagoziem Chima
Department of Computing, Oxford Brookes College of Chengdu University of Technology, Chengdu 610059, China.
Deep Learning and Intelligent Computing Lab, HACE SOFTTECH, Lagos 102241, Nigeria.
Diagnostics (Basel). 2023 Jan 13;13(2):299. doi: 10.3390/diagnostics13020299.
Breast cancer is one of the leading causes of death among women worldwide. Histopathological images have proven to be a reliable way to find out if someone has breast cancer over time, however, it could be time consuming and require much resources when observed physically. In order to lessen the burden on the pathologists and save lives, there is need for an automated system to effectively analysis and predict the disease diagnostic. In this paper, a lightweight separable convolution network (LWSC) is proposed to automatically learn and classify breast cancer from histopathological images. The proposed architecture aims to treat the problem of low quality by extracting the visual trainable features of the histopathological image using a contrast enhancement algorithm. LWSC model implements separable convolution layers stacked in parallel with multiple filters of different sizes in order to obtain wider receptive fields. Additionally, the factorization and the utilization of bottleneck convolution layers to reduce model dimension were introduced. These methods reduce the number of trainable parameters as well as the computational cost sufficiently with greater non-linear expressive capacity than plain convolutional networks. The evaluation results depict that the proposed LWSC model performs optimally, obtaining 97.23% accuracy, 97.71% sensitivity, and 97.93% specificity on multi-class categories. Compared with other models, the proposed LWSC obtains comparable performance.
乳腺癌是全球女性主要死因之一。组织病理学图像已被证明是一种可靠的方法，可长期确定某人是否患有乳腺癌，然而，人工观察时可能耗时且需要大量资源。为减轻病理学家的负担并挽救生命，需要一个自动化系统来有效分析和预测疾病诊断。本文提出一种轻量级可分离卷积网络（LWSC），用于从组织病理学图像中自动学习并分类乳腺癌。所提出的架构旨在通过使用对比度增强算法提取组织病理学图像的视觉可训练特征来解决低质量问题。LWSC模型实现了与不同大小的多个滤波器并行堆叠的可分离卷积层，以获得更宽的感受野。此外，还引入了因式分解以及利用瓶颈卷积层来减小模型维度。这些方法充分减少了可训练参数的数量以及计算成本，同时具有比普通卷积网络更强的非线性表达能力。评估结果表明，所提出的LWSC模型表现最优，在多类别上获得了97.23%的准确率、97.71%的灵敏度和97.93%的特异性。与其他模型相比，所提出的LWSC具有相当的性能。
