01669nas a2200229   4500000000100000000000100001008004100002260001200043653001900055653003900074653001200113653002400125653004000149100002200189700002400211245012900235856008000364300001000444490000600454520096500460022001401425       2022                            d  c06/202210aClassification10aConvolutional Neural Network (CNN)10aDropout10aData Pre-processing10aOrthopantomogram Radiography Images1 aAnuradha Laishram1 aKhelchandra Thongam00aAutomatic Classification of Oral Pathologies Using Orthopantomogram Radiography Images Based on Convolutional Neural Network  uhttps://www.ijimai.org/journal/sites/default/files/2022-05/ijimai_7_4_6.pdf  a69-770 v73 aAn attempt has been made to device a robust method to classify different oral pathologies using Orthopantomogram (OPG) images based on Convolutional Neural Network (CNN). This system will provide a novel approach for the classification of types of teeth (viz., incisors and molar teeth) and also some underlying oral anomalies such as fixed partial denture (cap) and impacted teeth. To this end, various image preprocessing techniques are performed. The input OPG images are resized, pixels are scaled and erroneous data are excluded. The proposed algorithm is implemented using CNN with Dropout and the fully connected layer has been trained using hybrid GA-BP learning. Using the Dropout regularization technique, over fitting has been avoided and thereby making the network to correctly classify the objects. The CNN has been implemented with different convolutional layers and the highest accuracy of 97.92% has been obtained with two convolutional layers.  a1989-1660