Mat-Isa Nor Ashidi, Mashor Mohd Yusoff, Othman Nor Hayati
Center for Electronic Intelligent System (CELIS), School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal, Penang, Malaysia.
Artif Intell Med. 2008 Jan;42(1):1-11. doi: 10.1016/j.artmed.2007.09.002. Epub 2007 Nov 8.
This paper proposes to develop an automated diagnostic system for cervical pre-cancerous. METHODS AND DATA SAMPLES: The proposed automated diagnostic system consists of two parts; an automatic feature extraction and an intelligent diagnostic. In the automatic feature extraction, the system automatically extracts four cervical cells features (i.e. nucleus size, nucleus grey level, cytoplasm size and cytoplasm grey level). A new features extraction algorithm called region-growing-based features extraction (RGBFE) is proposed to extract the cervical cells features. The extracted features will then be fed as input data to the intelligent diagnostic part. A new artificial neural network (ANN) architecture called hierarchical hybrid multilayered perceptron (H(2)MLP) network is proposed to predict the cervical pre-cancerous stage into three classes, namely normal, low grade intra-epithelial squamous lesion (LSIL) and high grade intra-epithelial squamous lesion (HSIL). We empirically assess the capability of the proposed diagnostic system using 550 reported cases (211 normal cases, 143 LSIL cases and 196 HSIL cases).
For evaluation of the automatic feature extraction performance, correlation test approach was used to determine the capability of the RGBFE algorithm as compared to manual extraction by cytotechnologist. The manual extraction of size was recorded in micrometer while the automatic extraction of size was recorded in number of pixels. Region color was recorded in mean of grey level value for both manual and automatic extraction. The results show that the estimated size and mean of grey level have strong linear relationship (correlation test more than 0.8) with those extracted manually by cytotechnologist. Hence, the size of nucleus, size of cytoplasm and grey level of cytoplasm created very strong linear relationship with correlation test more than 0.95 (approaching one). For the intelligent diagnostic, the performance of the H(2)MLP network was compared with three standard ANNs (i.e. multilayered perceptron (MLP), radial basis function (RBF) and hybrid multilayered perceptron (HMLP)). The performance was done based on accuracy, sensitivity, specificity, false negative and false positive. The H(2)MLP network performed the best diagnostic performance as compared to other ANNs. It was able to achieve 97.50% accuracy, 100% specificity and 96.67% sensitivity. The false negative and false positive were 1.33% and 3.00%, respectively.
This project has successfully developed an automatic diagnostic system for cervical pre-cancerous. This study has also successfully proposed one image processing technique namely the RGBFE algorithm for automatic feature extraction process and a new ANN architecture namely the H(2)MLP network for better diagnostic performance.
本文旨在开发一种用于宫颈癌前病变的自动诊断系统。
所提出的自动诊断系统由两部分组成；自动特征提取和智能诊断。在自动特征提取中，系统自动提取四个宫颈细胞特征（即细胞核大小、细胞核灰度、细胞质大小和细胞质灰度）。提出了一种新的特征提取算法，称为基于区域生长的特征提取（RGBFE）来提取宫颈细胞特征。然后将提取的特征作为输入数据输入到智能诊断部分。提出了一种新的人工神经网络（ANN）架构，称为分层混合多层感知器（H(2)MLP）网络，用于将宫颈癌前病变阶段预测为三个类别，即正常、低级别上皮内鳞状病变（LSIL）和高级别上皮内鳞状病变（HSIL）。我们使用550例报告病例（211例正常病例、143例LSIL病例和196例HSIL病例）对所提出的诊断系统的能力进行了实证评估。
为了评估自动特征提取性能，使用相关性测试方法来确定RGBFE算法与细胞技术专家手动提取相比的能力。手动提取的大小以微米记录，而自动提取的大小以像素数量记录。区域颜色在手动和自动提取中均以灰度值的平均值记录。结果表明，估计的大小和灰度平均值与细胞技术专家手动提取的结果具有很强的线性关系（相关性测试大于0.8）。因此，细胞核大小、细胞质大小和细胞质灰度与相关性测试的线性关系非常强，相关性测试大于0.95（接近1）。对于智能诊断，将H(2)MLP网络的性能与三种标准人工神经网络（即多层感知器（MLP）、径向基函数（RBF）和混合多层感知器（HMLP））进行了比较。性能基于准确性、敏感性、特异性、假阴性和假阳性进行评估。与其他人工神经网络相比，H(2)MLP网络表现出最佳的诊断性能。它能够达到97.50%的准确率、100%的特异性和96.67%的敏感性。假阴性和假阳性分别为1.33%和3.00%。
该项目成功开发了一种用于宫颈癌前病变的自动诊断系统。本研究还成功提出了一种图像处理技术，即用于自动特征提取过程的RGBFE算法，以及一种新的人工神经网络架构，即用于更好诊断性能的H(2)MLP网络。
