Table 2:

Neural network of the thyroid.

Author and year	Number of cases	Study design	Comments
Ippolito et al.[22] 2004	453 cases	A Feed-forward artificial neural network with 15-15-1 (input-hidden- output) design. The cytological features and clinical data were used as input nodes	ANN model can discriminate with higher sensitivity and specificity between benign and malignant nodules
Cochand-Priollet et al.[23] 2005	157 cases	The nuclear morphometric features were extracted from the nuclei and four important features were selected as input( roundness factor, standard deviation of the histogram, maximum value of the cooccurrence matrix, and mean value of the differences in histogram) The ANN model was used to distinguish benign and malignant thyroid tumors	ANN successfully discriminated benign and malignant lesions of thyroid
Shapiro et al.[24] 2007	197 cases	The cytologic features, nuclear morphometric data and chromatin texture analysis data were used to make the ANN model that can distinguish follicular adenoma and carcinoma	In 90% cases, ANN successfully identified the different types of follicular tumor
Varlatzidou et al.[25] 2011	335 cases	Size, shape, and texture of nuclei were used to make an ANN model to distinguish benign and malignant lesions of thyroid	ANN correctly identified the benign and malignant lesions
Savala et al.[7] 2018	57 cases	A back propagation ANN was designed as 31-5-1 (31 Input nodes. 5 Hidden nodes and 1 Output node) The cytological features and nuclear morphometric features were used as input nodes. ANN model was applied to distinguish follicular adenoma and carcinoma	ANN model successfully distinguished all cases of FA and FC
Sanyal et al.[26] 2018	87 cases	Convolutional neural network model was applied on the microphotographs of papillary carcinoma of the thyroid, and other non-papillary thyroid	The model showed good 90.48% sensitivity, and 83.33% specificity