Table 3:

Neural network of the gastrointestinal tract.

Author and year	Number of cases	Study design	Comments
Karakitsos et al.[27] 1996	23 cases of cancer, 19 of gastritis and 58 of ulcer	Morphometric and textural features of nuclei were used to make an ANN model	ANN correctly classified 97.6% of benign cells and 95% of malignant cells
Koss et al.[28] 1998	138 esophageal smears	PAPNET system was used	PAPNET identified many abnormal cells. It can be used as screening
Levine et al.[29] 1998	62 oral smears	PAPNET system was used	PAPNET screening methods correctly diagnosed squamous cell carcinoma in 61% of patients
Lai et al.[30] 2008	121 cases of gastric carcinomas	Clinical data and pathological findings were collected, and genetic polymorphisms of candidate genes data were used to build an ANN to predict tumor staging	ANN had an accuracy of 81.82%
Momeni-Boroujeni et al.[31] 2017	75 cases of pancreatic lesions (20 malignant, 24 benign, and 31 atypical)	A multi-layered perceptron was made on the basis of morphometric input data	The model was 100% accurate in unequivocal benign and malignant cases. However, it was 77% accurate in atypical cases

ANN: Artificial neural network