# 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. blob detection; dental caries dataset; deep learning; radiography +
Footnote †: journal: Medical  
Most of the people who are at risk of dental caries are low-income minorities, socially and economically less privileged, and uneducated people. Furthermore, clinical studies have shown that dental caries are caused or related to dietal changes and risk factors for cardiovascular disease [8]. Some retention and restoration methods have been introduced, proposed and improved to treat dental caries in the past. However, the diagnosis of dental caries has been difficult due to the various anatomical morphologies and different shapes of teeth restorations. In some teeth, it is difficult to detect caries at an early stage. Many of the carious lesions are detected at an advanced stage. Dental radiography is regarded as the most reliable diagnostic tool for detecting dental caries; however, most of its screening relies on empirical evidence results. The introduction of convolution neural networks in medical imaging analysis has shown excellent performance results. Medical imaging analysis has led to medical segmentation and diagnosis as one of the most important fields for pattern recognition and image processing. Some deep learning CNN models are used for detection and classification of skin cancer, knee cartilage, diabetic retinopathy, pulmonary tuberculosis and brain tumours among others. These models have shown very high performance accuracy, efficiency and promising clinical applications in various fields. However, there been very few studies that investigate detection and diagnosis in dentistry based on deep CNN model architectures. Furthermore, the research on detecting and diagnosis of dental caries is even more limited [9]. Accordingly, our approach presents a technique that evaluates the efficacy of deep learning methods for the detection of dental caries in bite-wing radiographs. We introduce the use of blob detection on bite-wing radiographs to detect dental caries. Blob detection is a mathematical method that detects specific regions in digital or radiographic images. Blobs are regions with notable differences with those in their neighbourhoods. Blobs are also referred to as regions that are either brighter or darker than their neighbourhood.