Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Bone Age Measurement using a Hybrid HCNN-KNN Model: A Case Study on Dental Panoramic Images

Version 1 : Received: 19 August 2021 / Approved: 20 August 2021 / Online: 20 August 2021 (12:06:57 CEST)

How to cite: Sharifonnasabi, F.; Jhanjhi, N.; Shamshirband, S.; John, J.; Alinejad Rokny, H. Bone Age Measurement using a Hybrid HCNN-KNN Model: A Case Study on Dental Panoramic Images. Preprints 2021, 2021080413 (doi: 10.20944/preprints202108.0413.v1). Sharifonnasabi, F.; Jhanjhi, N.; Shamshirband, S.; John, J.; Alinejad Rokny, H. Bone Age Measurement using a Hybrid HCNN-KNN Model: A Case Study on Dental Panoramic Images. Preprints 2021, 2021080413 (doi: 10.20944/preprints202108.0413.v1).

Abstract

Bone age measurement is the process of evaluating the level of skeletal maturity to estimate actual age of bone. This evaluation is usually done by comparing a radiograph of a bone with an existing standard chart that includes a set of identifiable images at each stage of development. Manual methods are based on the analysis of specific areas of bone images or dental structures. Both methods are highly dependent to human experience and are time-consuming. An automated model therefore is needed to estimate the age accurately. In this study, we propose a hybrid convolutional neural network (CNN) combining K nearest neighbours (KNN) and PCA to estimate the age of bone automatically and accurately. We applied our model, HCNN-KNN, on a dataset collected by dental teaching institutes and private dental clinics in Malaysia. A total of 1,922 panoramic dental radiographs of dental patients aged between 15 to 25 years old were obtained from the various centres. These radiographs were separated by age, classified as those in the range of 12-months, six-months, three-months, and one-month gaps. This novel investigation, implemented for the first time with precision to the range of the age for ± twelve months, ± six months, ± three months, and ± one month, and these age ranges determine the age of minors which could help the model to find better features and train the model more accurately. Replacing SoftMax with KNN generally improves traditional CNN performance to reduce the noises in images. Therefore, the optimal number of image similarities in a larger dataset is more significant, and the proposed method can benefit from large amounts of annotated data. Since the similarities of radiographic images are very similar, there may be several similar possibilities in the SoftMax classification method. These similar probabilities increase the risk of misdiagnosis of bone age measurements. Therefore, replacing KNN with SoftMax is the best choice for age group differentiation in classifiers. Finally, the accuracy rate is evaluated with the accuracy criterion according to the equation in confusion metrics and comparing existing models. The accuracy results on the dataset by ± 12 months, ± 6-months, ± 3-months, and ± 1-month are 99.98, 99.96, 99.87, and 98.78, respectively.

Keywords

Dental Age Measurement; Dental Radiography; Orthopantomogram; Convolutional Neural Network; K-Nearest Neighbour; Health Data Analytics; Biomedical Machine Learning

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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