A comparative study of three variations of the learning vector quantizer in the discrimination of benign from malignant gastric cells.
A prospective study was undertaken to investigate the potential value of morphometry and artificial neural networks (ANN) for the discrimination of benign and malignant gastric lesions. Two thousand five hundred cells from 23 cases of cancer, 19 cases of gastritis and 58 cases of ulcer were selected as a training set, and an additional 8524 cells from an equal number of cases of cancer, gastritis and ulcer were used as a test set. Images of routine processed gastric smears stained by the Papanicolaou technique were processed by a custom image analysis system. The application of the learning vector quantization (LVQ) classifier enabled correct classification of > 97% of benign cells and > 95% of malignant cells, obtaining an overall accuracy of > 97%. This study presents the capabilities of ANN, and also indicates that ANN and image morphometry may offer useful information on the potential of malignancy in gastric cells.
Karakitsos
P
P
Department of Clinical Cytology and Cytogenetics, Laiko Hospital, Athens, Greece.
Ioakim-Liossi
A
A
Pouliakis
A
A
Botsoli-Stergiou
E M
EM
Tzivras
M
M
Archimandritis
A
A
Kyrkou
K
K
D002454:Cell Differentiation; D002467:Cell Nucleus / Q000648:ultrastructure; D048429:Cell Size; D003720:Densitometry; D003937:Diagnosis, Differential; D005260:Female; D005756:Gastritis / Q000175:diagnosis / Q000473:pathology; D005773:Gastroscopy; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D016571:Neural Networks, Computer; D011446:Prospective Studies; D015203:Reproducibility of Results; D012984:Software; D013194:Staining and Labeling; D013274:Stomach Neoplasms / Q000175:diagnosis / Q000473:pathology*; D013276:Stomach Ulcer / Q000175:diagnosis / Q000473:pathology