In Orthodontics & craniofacial research
OBJECTIVE : A study of supervised automated classification of the Cervical Vertebrae Maturation (CVM) stages using deep learning network is presented. A parallel structured deep Convolutional Neural Network (CNN) with a preprocessing layer that takes X-ray images and the age as the input is proposed.
METHODS : A total of 1018 Cephalometric radiographs were labeled and classified according to the Cervical Vertebrae Maturation (CVM) stages. The images were separated according to gender for better model-fitting. The images were cropped to extract the cervical vertebrae automatically using an object detector. The resulting images and the age inputs were used to train the proposed Deep Learning (DL) model: AggregateNet with a set of tunable directional edge enhancers. After the features of the images are extracted, the age input is concatenated to the output feature vector. To have the parallel network not overfit, data augmentation is used. The performance of our CNN model was compared with other DL models ResNet20, Xception, MobileNetV2 and custom designed CNN model with the directional filters (CNNDF).
RESULTS : The proposed innovative model that uses a parallel structured network preceded with a preprocessing layer of edge enhancement filters achieved a validation accuracy of 82.35% in CVM stage classification on female subjects, 75.0% in CVM stage classification on male subjects, exceeding the accuracy achieved with the other DL models investigated. The effectiveness of the directional filters is reflected in the improved performance attained in the results. If AggregateNet is used without directional filters, the test accuracy decreases to 80.0% on female subjects and to 74.03% on male subjects.
CONCLUSION : AggregateNet together with the tunable Directional Edge Filters is observed to produce higher accuracy than the other models that we investigated in the fully automated determination of the CVM stages.
Atici Salih Furkan, Ansari Rashid, Allareddy Veerasathpurush, Suhaym Omar, Cetin Ahmet Enis, Elnagar Mohammed H
2023-Feb-28
Artificial Intelligence, Cervical Vertebrae Maturation Stages, Deep Learning, Growth and development, Multiple Input CNN
