Karakitsos P, Stergiou E B, Pouliakis A, Tzivras M, Archimandritis A, Liossi A, Kyrkou K
Department of Clinical Cytology and Cytogenetics, Laiko Hospital, Athens, Greece.
Anal Quant Cytol Histol. 1997 Apr;19(2):145-52.
To compare the accuracy of two different artificial neural networks (ANNs) for the discrimination of benign and malignant gastric lesions using morphometric and textural data on the nucleus.
Three thousand cells from 30 cancer cases, 26 cases of gastritis and 64 cases of ulcer were selected as a training set, and an additional 10,300 cells from equal cases of cancer, gastritis and ulcer were used as a test set using two different neural net architectures: back propagation (BP) and learning vector quantizer (LVQ). Images of routinely processed gastric smears stained by the Papanicolaou technique were processed by a custom image analysis system.
Application of the BP and three variations of the LVQ established correct classification of more than 97% of the benign cells and more than 95% of the malignant cells, obtaining an overall accuracy of more than 97%.
This study not only presents a comparative study of the abilities of ANNs but also indicates that the use of ANNs and image morphometry may offer useful information on the potential of malignancy of gastric cells.
使用细胞核的形态测量和纹理数据，比较两种不同人工神经网络（ANNs）对胃良性和恶性病变的鉴别准确性。
选取30例癌症病例、26例胃炎病例和64例溃疡病例中的3000个细胞作为训练集，并使用两种不同的神经网络架构：反向传播（BP）和学习向量量化（LVQ），从相同数量的癌症、胃炎和溃疡病例中选取另外10300个细胞作为测试集。用巴氏染色法常规处理的胃涂片图像由定制图像分析系统处理。
BP和LVQ的三种变体应用对超过97%的良性细胞和超过95%的恶性细胞进行了正确分类，总体准确率超过97%。
本研究不仅对人工神经网络的能力进行了比较研究，还表明使用人工神经网络和图像形态测量法可能为胃细胞恶性潜能提供有用信息。
