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Sharifonnasabi F, Jhanjhi NZ, John J, Obeidy P, Band SS, Alinejad-Rokny H, Baz M. Hybrid HCNN-KNN Model Enhances Age Estimation Accuracy in Orthopantomography. Front Public Health 2022; 10:879418. [PMID: 35712286 PMCID: PMC9197238 DOI: 10.3389/fpubh.2022.879418] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 04/22/2022] [Indexed: 11/17/2022] Open
Abstract
Age estimation in dental radiographs Orthopantomography (OPG) is a medical imaging technique that physicians and pathologists utilize for disease identification and legal matters. For example, for estimating post-mortem interval, detecting child abuse, drug trafficking, and identifying an unknown body. Recent development in automated image processing models improved the age estimation's limited precision to an approximate range of +/- 1 year. While this estimation is often accepted as accurate measurement, age estimation should be as precise as possible in most serious matters, such as homicide. Current age estimation techniques are highly dependent on manual and time-consuming image processing. Age estimation is often a time-sensitive matter in which the image processing time is vital. Recent development in Machine learning-based data processing methods has decreased the imaging time processing; however, the accuracy of these techniques remains to be further improved. We proposed an ensemble method of image classifiers to enhance the accuracy of age estimation using OPGs from 1 year to a couple of months (1-3-6). This hybrid model is based on convolutional neural networks (CNN) and K nearest neighbors (KNN). The hybrid (HCNN-KNN) model was used to investigate 1,922 panoramic dental radiographs of patients aged 15 to 23. These OPGs were obtained from the various teaching institutes and private dental clinics in Malaysia. To minimize the chance of overfitting in our model, we used the principal component analysis (PCA) algorithm and eliminated the features with high correlation. To further enhance the performance of our hybrid model, we performed systematic image pre-processing. We applied a series of classifications to train our model. We have successfully demonstrated that combining these innovative approaches has improved the classification and segmentation and thus the age-estimation outcome of the model. Our findings suggest that our innovative model, for the first time, to the best of our knowledge, successfully estimated the age in classified studies of 1 year old, 6 months, 3 months and 1-month-old cases with accuracies of 99.98, 99.96, 99.87, and 98.78 respectively.
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Affiliation(s)
- Fatemeh Sharifonnasabi
- Department of Computer Science & Engineering, School of Computing & IT (SoCIT), Taylor's University, Subang Jaya, Malaysia
| | - Noor Zaman Jhanjhi
- Department of Computer Science & Engineering, School of Computing & IT (SoCIT), Taylor's University, Subang Jaya, Malaysia
| | - Jacob John
- Department of Restorative Dentistry, Faculty of Dentistry, University of Malaya, Kuala Lumpur, Malaysia
| | - Peyman Obeidy
- Charles Perkins Centre, Faculty of Medicine and Health, University of Sydney, Darlington, NSW, Australia
| | - Shahab S Band
- Future Technology Research Centre, College of Future, National Yunlin University of Science and Technology, Yunlin, Taiwan
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, University of New South Wales (UNSW) Sydney, Kensington, NSW, Australia.,UNSW Data Science Hub, The University of New South Wales, UNSW Sydney, Kensington, NSW, Australia.,Health Data Analytics Program, AI-enabled Processes (AIP) Research Centre, Macquarie University, Macquarie Park, NSW, Australia
| | - Mohammed Baz
- Department of Computer Engineering, College of Computer and Information Technology, Taif University, Taif, Saudi Arabia
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