title: Artificial neural network in diagnosis of metastatic carcinoma in effusion cytology.
abstract: OBJECTIVE
  To build an artificial neural network (ANN) model for the detection of carcinoma in effusion cytology.
  
  
  METHODS
  We selected a total of 114 effusion cytology cases consisting of 57 each benign and malignant case. In all these cases, detailed cytological features, image morphometric data, densitometric data, and chromatin textural data were collected. Based on these data, we made a back propagation ANN model for diagnosing malignancy in effusion cytology. This network was designed as 25-2-1 (input nodes-hidden nodes-output node). Online back propagation method was applied for training the network. The training of the network was continued until the network error was reduced to 0.000654. Simultaneously, we also performed logistic regression (LR) analysis test to compare with ANN model performance.
  
  
  RESULTS
  ANN model worked excellent after adequate training. With the help of this model, it was possible to identify correctly all the malignant cases in validation and test set. The result of the multivariate LR analysis was also similar as that of ANN model and all the cases were also classified correctly.
  
  
  CONCLUSIONS
  In this study, we successfully constructed an ANN model to diagnose metastatic carcinoma in effusion cytology. ANN is very promising in the diagnosis of metastatic carcinoma in effusion cytology. In future, ANN model may help the cytopathologist to diagnose the difficult cases in effusion fluid.
authors.affiliation: Department of Cytopathology and Gynecological Pathology, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India.
authors.forename: Adarsh
authors.initials: A
authors.lastname: Barwad
authors.forename: Pranab
authors.initials: P
authors.lastname: Dey
authors.forename: Shaily
authors.initials: S
authors.lastname: Susheilia
mesh_terms: D003581:Cytodiagnosis / Q000379:methods; D003584:Cytological Techniques / Q000379:methods; D005434:Flow Cytometry / Q000379:methods*; D006801:Humans; D009362:Neoplasm Metastasis / Q000175:diagnosis*; D009369:Neoplasms / Q000175:diagnosis*; D016571:Neural Networks, Computer