Performance analysis of classification and segmentation of cysts in panoramic dental images using convolutional neural network architecture

Abstract

AbstractClassification of oral cysts is a crucial task as the similarity between cysts exists which requires a computer‐aided diagnosis system. Panoramic dental image is one of the widely used images to identify dental cyst, periodontal bone defects, periapical lesions, and pathological jaw lesions. This article proposes a modified LeNet architecture in a convolutional neural network for classifying the oral cyst images and a morphology‐based segmentation method for segmenting the cyst regions in the classified cyst images. A traditional data augmentation approach and a threefold cross‐validation method are used to increase the number of input samples and evaluate the accurate results respectively. The proposed methodology is applied to the cyst images obtained from a dental hospital. This model achieves a classification rate of 99.63% for cyst classification and demonstrates a sensitivity of about 98.3% for cyst segmentation. The proposed work has been compared with state‐of‐the‐art algorithms.