Neural network application in the discrimination of benign from malignant gastric cells.
OBJECTIVE
To investigate the potential value of morphometry and neural networks for the discrimination of benign from malignant gastric lesions.


METHODS
One thousand cells from 19 cases of cancer, 19 cases of gastritis and 56 cases of ulcer were selected as a training set, and an additional 4,000 cells from the same cases of cancer, gastritis and ulcer were used as a test set. Images of routinely processed gastric smears stained by the Papanicolaou technique were analyzed by a custom-made image analysis system.


RESULTS
Application of the neural network gave correct classification in 96% of benign cells and 89% of malignant cells.


CONCLUSIONS
The results indicate that the use of neural networks and image morphometry may offer useful information concerning the potential of malignancy in gastric cells.
Karakitsos
P
P
Department of Cytology, St. Olga Hospital, Greece.
Pouliakis
A
A
Koutroumbas
K
K
Stergiou
E B
EB
Tzivras
M
M
Archimandritis
A
A
Liossi
A I
AI
D003581:Cytodiagnosis / Q000379:methods; D003584:Cytological Techniques; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D005069:Evaluation Studies as Topic; D005756:Gastritis / Q000175:diagnosis / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016571:Neural Networks, Computer; D013272:Stomach Diseases / Q000175:diagnosis* / Q000473:pathology*; D013274:Stomach Neoplasms / Q000175:diagnosis / Q000473:pathology; D013276:Stomach Ulcer / Q000175:diagnosis / Q000473:pathology