Kar Subrata, Majumder Dwijesh Dutta
Department of Mathematics, Dumkal Institute of Engineering & Technology, Murshidabad, West Bengal, Pin-742406, India.
Department of Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, Pin-700108, India.
Pathol Oncol Res. 2019 Apr;25(2):777-790. doi: 10.1007/s12253-019-00582-8. Epub 2019 Feb 6.
This study aims to detect the abnormal growth of tissue in cervix region for diagnosis of cervical cancer using Pap test of patients. The proposed methodology classifies cervical cancer for pattern recognition either benign or malignant stages using shape and neuro-fuzzy based diagnostic model. In this experiment, firstly the authors segment Pap smear images of cervical cells using fuzzy c-means clustering algorithm and shape theory to classify them according to the presence of abnormality of the cells. Secondly the features extraction process is performed in the part of nucleus and cytoplasm on the squamous and glandular cells and the authors used input variables such as cytoplasm area (CA), cytoplasm circularity (CC), nucleus area (NA), nucleus circularity (NC), nucleus-cytoplasm ratio (NCR), and maximum nucleus brightness (MNB) in fuzzy tools and used fuzzy rules to evaluate the cervical cancer risk status as an output variable. The proposed neuro-fuzzy network system was developed for early detection of cervical cancer. A neural network was trained with 15-Pap image datasets where Levenberg-Marquardt(LM) a feed-forward back-propagation algorithm was used to get the status of the cervical cancer. Out of 15 samples database, 11 data set for training, 2 data set for validation and 2 data set for test were used in the ANN classification system. The presented fuzzy expert system(FES) successfully identified the presence of cervical cancer in the Pap smear images using the extracted features and the use of neuro-fuzzy system(NFS) for the identification of cervical cancer at the early stages and achieve a satisfactory performance with 100% accuracy.
本研究旨在通过对患者进行巴氏试验，检测宫颈区域组织的异常生长情况，以诊断宫颈癌。所提出的方法使用基于形状和神经模糊的诊断模型，对宫颈癌的良性或恶性阶段进行模式识别分类。在本实验中，作者首先使用模糊c均值聚类算法和形状理论对宫颈细胞的巴氏涂片图像进行分割，根据细胞异常情况对其进行分类。其次，在鳞状细胞和腺细胞的细胞核和细胞质部分进行特征提取，作者在模糊工具中使用细胞质面积（CA）、细胞质圆形度（CC）、细胞核面积（NA）、细胞核圆形度（NC）、细胞核-细胞质比率（NCR）和最大细胞核亮度（MNB）等输入变量，并使用模糊规则来评估宫颈癌风险状态作为输出变量。所提出的神经模糊网络系统用于早期检测宫颈癌。使用15个巴氏图像数据集对神经网络进行训练，其中使用Levenberg-Marquardt（LM）前馈反向传播算法来获取宫颈癌的状态。在15个样本数据库中，人工神经网络分类系统使用11个数据集进行训练，2个数据集进行验证，2个数据集进行测试。所提出的模糊专家系统（FES）使用提取的特征成功识别了巴氏涂片图像中宫颈癌的存在，并且使用神经模糊系统（NFS）在早期阶段识别宫颈癌，取得了100%准确率的满意性能。
