Comparative study of artificial neural networks in the discrimination between benign from malignant gastric cells.
OBJECTIVE
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.


METHODS
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.


RESULTS
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%.


CONCLUSIONS
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.
Karakitsos
P
P
Department of Clinical Cytology and Cytogenetics, Laiko Hospital, Athens, Greece.
Stergiou
E B
EB
Pouliakis
A
A
Tzivras
M
M
Archimandritis
A
A
Liossi
A
A
Kyrkou
K
K
D000328:Adult; D000368:Aged; D003581:Cytodiagnosis; D005260:Female; D005756:Gastritis / Q000175:diagnosis*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D013270:Stomach / Q000473:pathology*; D013274:Stomach Neoplasms / Q000175:diagnosis*; D013276:Stomach Ulcer / Q000175:diagnosis*