Dataset Augmentation for Machine Learning Applications of Dental Radiography

Abstract

The performance of any machine learning algorithm heavily depends on the quality and quantity of the training data. Machine learning algorithms, driven by training data can accurately predict and produce the right outcome when trained through enough amount of quality data. In the medical applications, being more critical, the accuracy is of utmost importance. Obtaining medical imaging data, enough to train machine learning algorithm is difficult due to a variety of reasons. An effort has been made to produce an augmented dental radiography dataset to train machine learning algorithms. 116 panoramic dental radiographs have been manually segmented for each tooth producing 32 classes of teeth. Out of 3712 images of individual tooth, 2910 were used for machine learning through general augmentation methods that include rotation, intensity transformation and flipping of the images, creating a massive dataset of 5.12 million unique images. The dataset is labeled and classified into 32 classes. This dataset can be used to train deep convolutional neural networks to perform classification and segmentation of teeth in x-rays, Cone-Beam CT scans and other radiographs. We retrained AlexNet on a subset of 80,000 images of the entire dataset and obtained classification accuracy of 98.88% on 10 classes. The retraining on original dataset yielded 88.31%. The result is evident of nearly a 10% increase in the performance of the classifier trained on the augmented dataset. The training and validation datasets include teeth affected with metal objects. The manually segmented dataset can be used as a benchmark to evaluate the performance of machine learning algorithms for performing tooth segmentation and tooth classification.

Tooth segmentation in 3D cone-beam CT images using deep convolutional neural network

“…We proposed a feedforward deep convolutional neural network with 5 sets of convolutional layers by batch normalization and ReLu layers. We trained the model with 116 2D panoramic dental radiographs [10]. The model yielded an accuracy of 80 % accuracy, surpassing VGG16 [24] which yielded 72 % accuracy in similar training conditions.…”

“…To enlarge our dataset, we applied traditional dataset augmentation techniques to surplus our dataset. The visual data augmentation techniques include rotating left and right to 10 degrees, horizontal flipping, and resizing [10]. For this research, we employed only the flipping technique.…”

“…-2D dental radiographs [10] -3D surface mesh optical scanning [28] -CBCT dental radiographs [2,20,4] The use of the type of scan depends on the condition of the patient and the need of the practitioner. The 2D dental radiographs provide basic X-ray imaging [10]. The 3D surface mesh is obtained through optical scanners.…”

A comparative study of X-ray and CT images in COVID-19 detection using image processing and deep learning techniques

“…The accuracy was improved from 88.15% to 89.08% using traditional augmentation and further increased to 95.66% using smart augmentation [26] . In [27] to perform teeth segmentation and classification, a dataset was created by applying image augmentation technique to the dental radiographs which increased the accuracy of the AlexNet model from 88.31% to 98.88% Table 1 . shows the increase in accuracy of the models after data augmentation.…”

Tooth Numbering and Condition Recognition on Dental Panoramic Radiograph Images Using CNNs

“…In addition to solving the overfitting problem, data augmentation can also provide positive feedback in the field of medical research. In the study of [27], if the data-augmented dataset is fed into deep learning for training, the accuracy of the test results can increase from 88.31% to 98.88%. In a study conducted by [28], the cone-beam CT image dataset was trained with data augmentation and input to a deep learning model to increase the test accuracy by up to 5%.…”