Learning compact and discriminative hybrid neural network for dental caries classification

• Published in: Microprocessors and microsystems Vol. 82; p. 103836
• Main Authors: Megalan Leo, L, Kalapalatha Reddy, T
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier B.V 01.04.2021; Elsevier BV
• Subjects: Artificial neural networks; Classification; Coders; Dental caries; Dentin; Diagnosis; Feature Extraction; Fluoridation; Hybrid neural network; Image classification; Image segmentation; Learning theory; Logistic regression layer; Machine learning; Medical imaging; Neural networks; Stacked sparse auto-encoder; Feature Extraction; Dental caries; Stacked sparse auto-encoder; Hybrid neural network; Classification; Logistic regression layer
• ISSN: 0141-9331; 1872-9436
• DOI: 10.1016/j.micpro.2021.103836

Dental caries, alternatively called tooth decay is considered as the most general and rapidly increasing diseases. According to the statistics, nearly 90% of the adults confront the issue of dental caries. Hence for proper dental care and health, early detection and diagnosis concerning the caries lesion plays a mandatory role. To achieve this, technique of Imaging Processing is being adopted which further helps the specialists to carry out precise diagnosis. Dental Caries can be categorized into different types depending on its position as well as seriousness. This categorization holds significant to carry out diagnosis and plan for the treatment of dental disease. The paper recommends the new HNN (Hybrid Neural Network) technique for detecting dental caries depending on, its type and therefore precisely classify the caries affected layer. HNN is a blend of ANN (Artificial Neural Network and DNN (Deep neural network) techniques. To obtain image classification, deep neural network relies upon the stacked sparse auto-encoder. Thereafter authentic information can be fetched from data via supervised fine-tuning and unsupervised pre-training. On the other hand ANN (Artificial neural network) relies upon the logistic regression for classifying the dental caries affected layer. Various processes involved in dental input image are as following: l. Pre-Processing, 2. Segmentation, 3. Feature Extraction and 4.Classification. Caries are categorized into 4 different layers by the HNN, namely: Enamel, Dentin, Pulp and root lesions. The proposed system yield in effective output against the input image provided and precisely categorizes the caries level. Hybrid neural network is the innovative technique which is giving good result in terms of accuracy and processing time.