基于蚁群优化的猫乳腺肿瘤检测

Hossam M. Moftah, Mohammad Ibrahim, A. Hassanien, G. Schaefer
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引用次数: 0

摘要

乳腺肿瘤是猫体内最常见的肿瘤之一。超过85%的猫乳腺肿瘤是恶性的,它们倾向于迅速生长和转移到不同的器官,如肺和淋巴结。与人类的乳腺肿瘤类似,它们一开始只是乳腺中的一个小肿块,如果不及时发现和治疗,就会逐渐增大。在本文中,我们提出了一种方法来检测宽腺瘤乳腺肿瘤的猫使用蚁群优化。然后可以从分割的图像区域中提取图像特征。为了评估我们所提出的方法的性能,我们从三个猫病例的阔腺瘤的组织载玻片上取下了25张显微图像。实验结果表明,该系统具有较高的有效性和性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mammary Gland Tumor Detection in Cats Using Ant Colony Optimisation
Mammary gland tumors are among the most common tumors in cats. Over 85 percent of mammary tumors in cats are malignant and they tend to grow and metastasize quickly to different organs like lungs and lymph nodes. Similar to breast tumors in humans, they start as a small lump in a mammary gland and then grow and increase in size unless detected and treated. In this paper, we present an approach to detect broadenoma mammary gland tumors in cats using ant colony optimisation. Image features can then be extracted from the segmented image regions. To evaluate the performance of our presented approach, 25 microscopical images were taken from tissue slides of broadenomas from three cat cases. The experimental results obtained confirm that the effectiveness and performance of the proposed system is high.
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