A Deep Mathematical Morphological Neural Network for the Classification of Periapical Radiographs in the Diagnosis of Dental Diseases
Abstract
The importance of medical imaging cannot be overemphasized as it is one of the best ways to diagnose a disease in medical practice objectively. In dentistry, no imaging means no objective diagnosis for it is with imaging dentists ?nd hidden dental structure, bone loss, malignant or benign masses, and other dental diseases that cannot be discovered or examined during a visual examination. The use of dental radiographs also helps dentists to detect hidden dental diseases early. This model was developed integrating mathematical morphology (MM) operations (dilation, erosion, opening and closing) in the convolution layer of convolutional neural network (CNN), for data preprocessing and quality feature extraction. With its high sense of intelligence (artificial) obtained during training, the system receives dental images and analyses them automatically for various clinical findings with which 6 dental disease problems were solved. With an achieved accuracy of 99.78%, it can be established that this system can be used in dental clinics with high confidence giving very little or no-error-diagnosis. To make this system more scalable and robust, more dental diseases be added through other MM based theory like lattice, topology and random functions other than set theory-based MM used in this study.
Keywords
Mathematical Morphology (MM)
Dilation
Erosion
Opening
Closing
References
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