Automatic Blob Detection for Dental Caries

Abstract

Dental Caries are one of the most prevalent chronic diseases around the globe. Detecting carious lesions is a challenging task. Conventional computer aided diagnosis and detection methods in the past have heavily relied on the visual inspection of teeth. These methods are only effective on large and clearly visible caries on affected teeth. Conventional methods have been limited in performance due to the complex visual characteristics of dental caries images, which consist of hidden or inaccessible lesions. The early detection of dental caries is an important determinant for treatment and benefits much from the introduction of new tools, such as dental radiography. In this paper, we propose a deep learning-based technique for dental caries detection namely: blob detection. The proposed technique automatically detects hidden and inaccessible dental caries lesions in bitewing radio-graphs. The approach employs data augmentation to increase the number of images in the data set to have a total of 11,114 dental images. Image pre-processing on the data set was through the use of Gaussian blur filters. Image segmentation was handled through thresholding, erosion and dilation morphology, while image boundary detection was achieved through active contours method. Furthermore, the deep learning based network through the sequential model in Keras extracts features from the images through blob detection. Finally, a convexity threshold value of 0.9 is introduced to aid in the classification of caries as either present or not present. The process of detection and classifying dental caries achieved the results of 97% and 96% for the precision and recall values, respectively.

Dental Caries Detection Using Score-Based Multi-Input Deep Convolutional Neural Network

“…They used 11,114 tooth images obtained using data augmentation methods based on periapical dental X-ray images. As a result, the proposed model for caries detection was 97% successful [21]. Vinayahalingam et al carried out a study based on the evaluation of the classification performance of deep learning architectures using panoramic x-ray images.…”

Peptide-Functionalized ZnSe:Mn Quantum Dots as Fluorescent Probes for Accurate Localization of Hidden Dental Lesion Sites

“…Progression through these stages signifies the extent and severity of decay, highlighting the importance of early detection and intervention . Therefore, there is an urgent need to develop a detection method that is simple, fast, low-cost, and capable of early and precise localization of dental lesion sites. − …”

Classification of Lateral Incisor Teeth for Aesthetic Dentistry Using Intelligent Techniques

“…These parameters include the midline, the length of the upper incisors, the size of the teeth, the various shapes of the incisor teeth, and the way the size, texture, and transitional angles of the teeth look. Other parameters include the midline, the length of the lower incisors, the size of the teeth, and the different shapes of the incisor teeth [4].…”

“…The accuracy and classification rate of the results obtained by the four deep learning-adapted techniques are as follows: YOLOV3 (You Only Look Once, Version 3) with 83.4% and 60.7%, quicker R-CNN (Region-based Convolutional Neural Network) with 87.4% and 67.8%, Retinanet with 83% and 65.7%, and lastly SSD with 83% and 68.8% in year 2022. [4] V. Majanga and S. Viriri Automatic Blob Detection for Dental Caries, created a database of 11,114 teeth using dental radiographs by applying a Gaussian blur filter and utilizing erosion and dilation morphology. The study aimed to identify instances of dental caries, and the findings revealed the following information: The value for precision is equivalent to 97%, while the value for the recall is 96% in year 2021.…”