利用组织病理学图像的混合轻量级乳腺癌分类框架

IF 5.3 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Daniel Addo , Shijie Zhou , Kwabena Sarpong , Obed T. Nartey , Muhammed A. Abdullah , Chiagoziem C. Ukwuoma , Mugahed A. Al-antari
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引用次数: 0

摘要

诊断乳腺癌的一个关键因素是使用高效、轻便和精确的分类方法。卷积神经网络(CNN)作为一种对组织病理学图像进行分类的可行方法备受关注。然而,更深、更广的模型往往依赖于一阶统计,需要大量的计算资源,并且难以使用固定的内核维度,这限制了对不同分辨率数据的处理,从而降低了模型在测试过程中的性能。本研究介绍了用于乳腺组织病理学图像分类的新型轻量级人工智能(AI)模型 BCHI-CovNet。首先,本文提出了一种新颖的多尺度深度可分离卷积。它将输入张量分割成不同的张量片段,每个片段都有独特的内核大小,在一个深度卷积中整合各种内核大小,以捕捉低分辨率和高分辨率模式。其次,还引入了一个额外的池化模块,以捕捉跨信道和空间维度的大量二阶统计信息。该模块与创新的多头自我关注机制协同工作,捕捉对学习过程有重大贡献的长距离像素,产生独特的判别特征,进一步丰富表征,并在训练过程中引入像素多样性。这些新颖的设计大大降低了模型参数和 FLOP 的计算复杂度,这对于资源有限的医疗设备来说至关重要。在两个可公开获取的乳腺癌组织病理学图像数据集上采用所建议的模型所取得的结果显示了值得注意的性能。具体来说,在 BreaKHis 数据集上,所建议的方法达到了很高的准确率:放大 40 倍时为 99.15%,放大 100 倍时为 99.08%,放大 200 倍时为 99.22%,放大 400 倍时为 98.87%。此外,它在 BACH 数据集上的准确率也达到了 99.38%。这些结果凸显了 BCHI-CovNet 在乳腺癌组织病理学图像分类方面的卓越功效和实用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A hybrid lightweight breast cancer classification framework using the histopathological images

A hybrid lightweight breast cancer classification framework using the histopathological images

A crucial element in the diagnosis of breast cancer is the utilization of a classification method that is efficient, lightweight, and precise. Convolutional neural networks (CNNs) have garnered attention as a viable approach for classifying histopathological images. However, deeper and wider models tend to rely on first-order statistics, demanding substantial computational resources and struggling with fixed kernel dimensions that limit encompassing diverse resolution data, thereby degrading the model’s performance during testing. This study introduces BCHI-CovNet, a novel lightweight artificial intelligence (AI) model for histopathological breast image classification. Firstly, a novel multiscale depth-wise separable convolution is proposed. It is introduced to split input tensors into distinct tensor fragments, each subject to unique kernel sizes integrating various kernel sizes within one depth-wise convolution to capture both low- and high-resolution patterns. Secondly, an additional pooling module is introduced to capture extensive second-order statistical information across the channels and spatial dimensions. This module works in tandem with an innovative multi-head self-attention mechanism to capture the long-range pixels contributing significantly to the learning process, yielding distinctive and discriminative features that further enrich representation and introduce pixel diversity during training. These novel designs substantially reduce computational complexities regarding model parameters and FLOPs, which is crucial for resource-constrained medical devices. The outcomes achieved by employing the suggested model on two openly accessible datasets for breast cancer histopathological images reveal noteworthy performance. Specifically, the proposed approach attains high levels of accuracy: 99.15 % at 40× magnification, 99.08 % at 100× magnification, 99.22 % at 200× magnification, and 98.87 % at 400× magnification on the BreaKHis dataset. Additionally, it achieves an accuracy of 99.38 % on the BACH dataset. These results highlight the exceptional effectiveness and practical promise of BCHI-CovNet for the classification of breast cancer histopathological images.

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来源期刊
CiteScore
16.50
自引率
6.20%
发文量
77
审稿时长
38 days
期刊介绍: Biocybernetics and Biomedical Engineering is a quarterly journal, founded in 1981, devoted to publishing the results of original, innovative and creative research investigations in the field of Biocybernetics and biomedical engineering, which bridges mathematical, physical, chemical and engineering methods and technology to analyse physiological processes in living organisms as well as to develop methods, devices and systems used in biology and medicine, mainly in medical diagnosis, monitoring systems and therapy. The Journal''s mission is to advance scientific discovery into new or improved standards of care, and promotion a wide-ranging exchange between science and its application to humans.
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