Machine learning (ML) has emerged as a powerful tool for pattern recognition. Traditional ML algorithms have limited ability to reveal the most sophisticated features of cancer histopathological images, but their robustness and fault tolerance can be enhanced by using fuzzy modeling to capture the uncertainty in image data. Therefore, this article proposes a novel CoMHisP framework based on a fuzzy support vector machine with within-class density information (FSVM-WD). It utilizes a novel feature extraction technique by optimizing the block size to extract image micropatterns and computing center of mass (CoM) for each pixel to extract feature vectors. The performance of the proposed framework is evaluated using a CMTHis dataset comprising histopathological images of canine mammary tumor (CMT), a prevalent neoplastic disease in female dogs, and an established model for human breast cancer. Data analysis reveals that stain normalization and magnification influence the performance of the CoMHisP framework, with the best results achieved at lower magnifications after stain normalization. The proposed framework achieves a classification accuracy of 97.25% ($\pm$1.80%) using a FSVM-WD classifier, outperforming both traditional ML and deep FE-VGGNET16-based feature descriptors. To the best of our knowledge, this is the first time a CoM-based feature descriptor has been proposed for histopathological image analysis of CMTs and its performance was evaluated using a fuzzy SVM-based classifier. The proposed method performs well with datasets of limited size and low-magnification images and, therefore, has the potential to provide rapid and accurate diagnosis in low-cost clinical settings.

中文翻译：

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CoMHisP：一种基于模糊 SVM 的具有类内相对密度的组织病理学图像分类新特征提取器

机器学习 (ML) 已成为模式识别的强大工具。传统的 ML 算法揭示癌症组织病理学图像最复杂特征的能力有限，但可以通过使用模糊建模来捕获图像数据中的不确定性来增强其鲁棒性和容错性。因此，本文提出了一种基于具有类内密度信息的模糊支持向量机（FSVM-WD）的新型 CoMHisP 框架。它利用一种新颖的特征提取技术，通过优化块大小来提取图像微图案并计算每个像素的质心 (CoM) 以提取特征向量。使用包含犬乳腺肿瘤 (CMT) 的组织病理学图像的 CMTHis 数据集评估所提出框架的性能，这是一种在雌性犬中普遍存在的肿瘤疾病，以及已建立的人类乳腺癌模型。数据分析表明，染色归一化和放大倍数会影响 CoMHisP 框架的性能，染色归一化后在较低放大倍数下获得最佳结果。所提出的框架使用 FSVM-WD 分类器实现了 97.25%（$\pm$1.80%）的分类准确率，优于传统的 ML 和基于深度 FE-VGGNET16 的特征描述符。据我们所知，这是第一次提出基于 CoM 的特征描述符用于 CMT 的组织病理学图像分析，并使用基于模糊 SVM 的分类器评估其性能。所提出的方法在有限大小和低放大率图像的数据集上表现良好，因此有可能在低成本的临床环境中提供快速准确的诊断。