Karakitsos P, Stergiou E B, Pouliakis A, Tzivras M, Archimandritis A, Liossi A I, Kyrkou K
Department of Clinical Cytology and Cytogenetics, Laiko Hospital, Athens, Greece.
Anal Quant Cytol Histol. 1996 Jun;18(3):245-50.
To investigate the potential value of morphometry and the back propagation neural network for the discrimination of benign and malignant gastric lesions.
The study group consisted of 23 cases of cancer, 19 of gastritis and 58 of ulcer. Images of routinely processed gastric smears stained by the Papanicolaou technique were processed by a custom image analysis system. Analysis of the images gave a data set of 11,024 cells. Two different neural net architectures were used to classify benign from malignant cells based on the extracted morphometric and textural features. The data from 2,500 randomly selected cells were used as a training set, and the data from the remaining 8,524 cells were applied as a test set.
Application of the back propagation neural network permitted the correct classification of 97.6% of benign cells and 95% of malignant cells with overall accuracy 97.3%.
These results indicate that neural networks and image morphometry may offer useful information about the potential for malignancy in gastric cells.
探讨形态测量学和反向传播神经网络在鉴别胃良性和恶性病变中的潜在价值。
研究组包括23例癌症患者、19例胃炎患者和58例溃疡患者。采用巴氏染色技术对常规处理的胃涂片图像进行定制图像分析系统处理。图像分析得到了一个包含11024个细胞的数据集。基于提取的形态测量和纹理特征，使用两种不同的神经网络结构对良性和恶性细胞进行分类。从2500个随机选择的细胞中获取的数据用作训练集，其余8524个细胞的数据用作测试集。
应用反向传播神经网络可正确分类97.6%的良性细胞和95%的恶性细胞，总体准确率为97.3%。
这些结果表明，神经网络和图像形态测量学可能为胃细胞恶性潜能提供有用信息。
