基于人工蜂群算法的改进自适应神经模糊推理系统(MANFIS)骨癌分类优化

Ali Lefteh, M. Houshmand, Mahsa Khorrampanah, G. Smaisim
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引用次数: 15

摘要

骨癌是骨骼组织的不均匀发展。这种疾病可以是原发性的,也可以是继发性的。原发性骨肉瘤由骨细胞生长,而继发性骨癌则从不同的身体器官开始,然后扩散到骨细胞。鱼片癌的治愈取决于肿瘤的定位、大小和其他因素。因此,对骨癌的初步识别和分类已成为治疗患者的必要条件。在此过程中,提出了一种基于模糊c均值聚类的骨癌分类技术,同时采用了基于人工蜂群算法的修正自适应神经模糊推理系统(MANFIS)对骨癌进行良恶性分类。实验结果表明,该方法具有96.67%的准确率,在骨磁共振图像中具有更高的生产率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization of Modified Adaptive Neuro-Fuzzy Inference System (MANFIS) with Artificial Bee Colony (ABC) Algorithm for Classification of Bone Cancer
Bone cancer is the uneven progress of tissue in the bone. This illness can be primary or secondary. Primary bone sarcoma grows from the cells of the bone, while secondary bone cancer begins from different body organs and then extends to the bone cells. The cure of fillet cancer depends on tumor localization, size, and other factors. Therefore, initial recognition and category of bone cancer have become required to therapy the patient. In this procedure, a technique proposed for the glory of bone cancer employing fuzzy C-mean clustering also uses Modified Adaptive Neuro-Fuzzy Inference System (MANFIS) with Artificial Bee Colony algorithm to classify benign and malignant bone cancer. The experiments illustrate that the suggested method has a high accuracy of 96.67% and offers enhanced productivity than the systems in bone MR images.
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