Digital image processing (DIP) is the process of transforming a digital image using a set of algorithms. It includes simple tasks like picture filtration, as well as more complicated tasks like image segmentation, classifications, emotion identification, anomaly detection, and more. Image segmentation is the process of dividing a digital image into many subgroups based on the pixels known as image objects, which can minimize the image's complexity and thus make image analysis easier. It has been utilized in the medical profession for effective and faster diagnosis, as well as the detection of illnesses, tumors, and cell and tissue patterns obtained from various medical imaging techniques such as radiography, MRI, CT scan, ultrasound, and so on.
The proposed method, which requires segmentation of mandibular premolar teeth before image classification, has some flaws that need to be worked out in future studies. The original images for digital panoramic dental imaging were of various sizes and resolutions. The input images should be normalized as a result. When the chosen parameters affect image classification accuracy, using the proper image normalization techniques is crucial. Resizing an image, for example, may increase its general size but reduce its resolution and distort the edges of the ROI, lowering classification accuracy. Furthermore, the dental developmental stages A and B were omitted and will not be tested due to the limited number of datasets. It is because the dental development has proceeded to the stage of the lower-bound chronological age (stages A and B). Hence, the input dataset for image classification only involved the image of mandibular premolars' developmental stage from stage C onwards until stage H.
The quality of digital panoramic dental imaging varies greatly from one patient to another, depending on the patient's position during the treatment as well as the expertise of the human operators. As a result, rather of going through the automated procedure, a semi-automated technique has been implemented. For example, getting a decent quality image requires proper placement of a bite-blocker and the patient's head. Furthermore, the panoramic x-ray can give a somewhat fuzzy image from time to time, making precise measurements of your teeth and jaw problematic. As a result, due to the wide range of data sources, developing a fully automated system for age estimation can be difficult.
Image segmentation is employed in this study to segment the first and second mandibular of permanent teeth before performing image classification, based on the adaptability of digital image processing technique. In this entire semi-automated approach, the use of the DP-AC method has proven to be successful. The easy steps to implement the DP-AC method are presented in Fig. 5, making this approach handleable by the end-user. Rather than constructing a completely automated system, which would require a considerable financial investment, a semi-automated system has produced promising results and satisfactory performance in the assessment of dental age.
The dental age for permanent teeth can be estimated by monitoring dental calcification development using radiographic images. Demirjian et al. suggested the staging of teeth based on the development of the teeth' outline instead of its proportions using the lower-left seven permanent teeth, except the third molar. The implementation of DCNN for the classification of dental stages yields promising results as the accuracy of the classification obtained is very high in some of the predicted stages.
Misclassification has occurred due to the variety of factors that may be linked to the deep neural network's effectiveness and the significance of assigned parameters or other factors related to dental morphology that affect the neural network's ability to achieve a useful classification. The proposed DCNN model was explicitly built based on our datasets. The experiment was performed by assigning parameters to a network model involving layer sizes, several dense layers and convolution layers. As a result, the stage classification accuracy obtained was 0.78. A pre-trained model of CNN which are DenseNet201 and AlexNet was adapted by Merdietio et al. [15], Banar et al. [28] and De Tobel et al. [14], respectively. Based on the performance, the proposed method performed superior compared to the other three methods, which are 0.61, 0.54 and 0.51, respectively.
Moreover, the misclassification of the test datasets is likely due to the behavior of the dataset itself. For example, the new test data implemented did not adequately reflect the broader domain cases. Therefore, this would potentially impact the accuracy of the test. However, based on Table 4, the proposed DCNN model looks promising as more than 90% of the test data in stages C, G and H were correctly allocated. Lower accuracies in stages D, E and F may be due to the significant variation of the morphological structure of dentition between stages. As the human interpretations of allocated stages are highly dependent on skills and experiences, a mutual agreement could not be achieved in some observation samples. Hence, the kappa value obtained is 0.58, indicating moderate agreement. However, most misclassified stages were seen only in the neighboring stages. Although it was not a perfect agreement, the proposed DCNN model showed a robust network. There is no sign of whether the model is over-or underfitting detected during the learning process, whereby the training accuracy was noted higher than the validation and testing accuracy.
Common challenges in deep learning models include the lack of data available for training, model overfitting, model underfitting and high training time. In this research, data augmentation techniques involving image resizing, rescaling, spinning, flipping, cropping, filtering, and brightness modification have been used to increase the number of training datasets. This technique is achieved using the open-source Python preprocessing package known as Scikit-image. Therefore, the model was able to perform well and escape under-fitting in the validation collection.
Model overfitting is the most common problem that data scientist has faced in the field of machine learning [29]. Introduction of the dropout feature to the design of the model is one of the methods used to resolve the overfitting problem. Some of the neurons in the neural network were switched off using the dropout. For example, in an experiment, a drop of 0.1 to a layer initially had 30 neurons that removed three neurons out of its total number of neurons. As a result, a less complicated architecture was obtained, and the model will not learn the intricate pattern. Overall, it can be argued that the DCNN structure plays a critical role in the classification process as it determines the overall performance of the automated stage allocation.