Multiple Adaptive Neuro-Fuzzy Inference System with Automatic Features Extraction Algorithm for Cervical Cancer Recognition
Multiple Adaptive Neuro-Fuzzy Inference System with Automatic Features Extraction Algorithm for Cervical Cancer Recognition
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DOI：
10.1155/2014/181245
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发表时间：
2014-01-01
影响因子：
--
通讯作者：
Al-Betar, Mohammed Azmi
中科院分区：
文献类型：
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作者：
Al-Batah, Mohammad Subhi;Isa, Nor Ashidi Mat;Al-Betar, Mohammed Azmi
到目前为止，宫颈癌仍然是全球女性癌症相关死亡的主要原因。目前筛查宫颈癌的方法(即巴氏涂片和液基细胞学(LBC))既耗时又依赖于细胞病理学家的技术，因此相当主观。因此，本文提出了一种智能计算机视觉系统来帮助病理学家克服这些问题，从而产生更准确的结果。开发的系统分为两个阶段。在第一阶段，执行自动特征提取(AFE)算法。第二阶段提出了一种用于识别过程的神经模糊模型--多自适应神经模糊推理系统(MANFIS)。MANFIS包含一组并行组合的ANFIS模型，以产生具有多输入多输出结构的模型。该系统能够将宫颈细胞图像分为三组，即正常、低度鳞状上皮内病变(Lsil)和高度鳞状上皮内病变(HSIL)。实验结果表明，AFE算法具有与人工抽取相同的效果，而MANFIS的分类性能良好，分类正确率达到94.2%。
To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy.