Age-Net: An Advanced Hybrid Deep Learning Model for Age Estimation Using Orthopantomograph Images - Baydogan, Merve Parlak; Baybars, Sumeyye Cosgun; Tuncer, Seda Arslan (Int Information & Engineering Technology Assoc, 2023)
Forensic odontology, recognized as a fundamental and reliable technique in human identification, frequently employs orthopantomograph images in dental biometry. Despite the introduction of various techniques for age and identity estimation, the accurate and rapid interpretation of these images remains challenging. Manual methods, currently employed by forensic experts, present numerous limitations including time consumption, human error, and challenges in handling large data sets. Addressing these limitations, this study proposes a computer-aided hybrid age detection system, Age-Net, leveraging artificial intelligence. A total of 933 orthopantomograph images, categorized into three classes, were collected from Firat University Hospital for this study. These images were subsequently resized to be compatible with pre-trained Convolutional Neural Networks (CNNs) models, such as AlexNet, ResNet50, VGG16, SqueezeNet, EfficientNetB0, DenseNet201, and ResNet18. Following the extraction of feature vectors from these images, algorithms including Naive Bayes (NB), K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), XGBoost Algorithm, Support Vector Machine (SVM), Decision Tree (DT), and Linear Discriminant (LD) were implemented for detection. The use of an array of feature extraction models and algorithms aimed to best represent the features of the dataset, thereby enhancing classification performance. The proposed system's efficacy was assessed and validated using the 5-fold cross-validation test technique and statistical Friedman test. Of all models, the EfficientNetB0-SVM hybrid model demonstrated superior performance, achieving highest accuracy, precision, sensitivity, F-score, and AUC ratios of 0.846, 0.850, 0.846, 0.846, and 0.970, respectively. This hybrid detection system, Age-Net, is projected to provide time and cost benefits to forensic experts in their clinical studies. However, the data utilized in this study are limited to specific age groups. Future research could expand the number of age groups and data to observe potential enhancements in the system's success rate.
Keywords: age estimation; deep learning; dental orthopantomographic image; machine learning