AIMS AND OBJECTIVE: To build up an artificial neural network (ANN) model in the diagnosis of urothelial cell carcinoma (UCC) in urine cytology smears. MATERIAL AND METHODS: We randomly selected a total of 115 urine cytology samples, out of which 59 were histopathology proven UCC cases and remaining 56 were benign cases from routine cytology samples. All the carcinoma cases were proven on histopathology. Image morphometric analysis was performed on Papanicolaou's stained smears to study nuclear area, diameter, perimeter, standard deviation of nuclear area, and integrated gray density. Detailed cytological features were also studied in each case by two independent observers and were semi-quantitatively graded. The back propagation ANN model was designed as 17-11-3 with the help of heuristic search. The cases were randomly partitioned as training, validation, and testing sets by the program. There were 79 cases for training set, 18 cases for validation set and 18 cases for test set. RESULT: In the training set, ANN was able to diagnose all the malignant and benign cases. In the test set, all the benign and malignant cases were diagnosed correctly. However, one of the low grade cases was diagnosed as high grade UCC by ANN model. CONCLUSIONS: We successfully built an ANN model in urine from the visual and morphometric data to identify the benign and malignant cases. In addition, the system can also identify the low grade and high grade UCC cases.
AIMS AND OBJECTIVE: To build up an artificial neural network (ANN) model in the diagnosis of urothelial cell carcinoma (UCC) in urine cytology smears. MATERIAL AND METHODS: We randomly selected a total of 115 urine cytology samples, out of which 59 were histopathology proven UCC cases and remaining 56 were benign cases from routine cytology samples. All the carcinoma cases were proven on histopathology. Image morphometric analysis was performed on Papanicolaou's stained smears to study nuclear area, diameter, perimeter, standard deviation of nuclear area, and integrated gray density. Detailed cytological features were also studied in each case by two independent observers and were semi-quantitatively graded. The back propagation ANN model was designed as 17-11-3 with the help of heuristic search. The cases were randomly partitioned as training, validation, and testing sets by the program. There were 79 cases for training set, 18 cases for validation set and 18 cases for test set. RESULT: In the training set, ANN was able to diagnose all the malignant and benign cases. In the test set, all the benign and malignant cases were diagnosed correctly. However, one of the low grade cases was diagnosed as high grade UCC by ANN model. CONCLUSIONS: We successfully built an ANN model in urine from the visual and morphometric data to identify the benign and malignant cases. In addition, the system can also identify the low grade and high grade UCC cases.