Potential of the back propagation neural network in the discrimination of benign from malignant gastric cells.
OBJECTIVE
To investigate the potential value of morphometry and the back propagation neural network for the discrimination of benign and malignant gastric lesions.


METHODS
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.


RESULTS
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%.


CONCLUSIONS
These results indicate that neural networks and image morphometry may offer useful information about the potential for malignancy in 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 I
AI
Kyrkou
K
K
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002452:Cell Count; D048429:Cell Size; D005260:Female; D005756:Gastritis / Q000175:diagnosis / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D013274:Stomach Neoplasms / Q000175:diagnosis* / Q000473:pathology; D013276:Stomach Ulcer / Q000175:diagnosis / Q000473:pathology