Multi Oral Disease Classification from Panoramic Radiograph using Transfer Learning and XGBoost

Abstract

The subject of oral healthcare is a crucial research field with significant technological development. This research examines the field of oral health care known as dentistry, a branch of medicine concerned with the anatomy, development, and disorders of the teeth. Good oral health is essential for speaking, smiling, testing, touching, digesting food, swallowing, and many other aspects, such as expressing a variety of emotions through facial expressions. Comfort in doing all these activities contributes to a person's self-confidence. For diagnosing multiple oral diseases at a time panoramic radiograph is used. Oral healthcare experts are important to appropriately detect and classify disorders. This automated approach was developed to eliminate the overhead of experts and the time required for diagnosis. This research is based on a self-created dataset of 500 images representing six distinct diseases in 46 possible combinations. Tooth wear, periapical, periodontitis, tooth decay, missing tooth, and impacted tooth are all examples of diseases. This system is developed using the concept of transfer learning with the use of a different pre-trained network such as "ResNet50V2", "ResNet101V2", "MobileNetV3Large", "MobileNetV3Small", "MobileNet", "EfficientNetB0", "EfficientNetB1", and "EfficientNetB2" with XGBoost and to get the final prediction The images in the dataset were divided into 80% training and 20% images for testing. To assess the performance of this system, various measuring metrics are used. Experiments revealed that the proposed model detected Tooth wear, periapical, periodontitis, tooth decay, missing tooth, and impacted tooth with an accuracy of 91.8%, 92.2%, 92.4%, 93.2%, 91.6%, and 90.8%, respectively.

Highly Accurate Deep Learning Model for Olive Leaf Disease Classification: A Study in Tacna-Per´u

“…The results obtained reaffirm the findings regarding the use of Data Augmentation [17], [18], [19], Transfer Learning [20], [21], [22], and Fine-Tuning [23], [24]. In addition, our extensive experimental comparative study introduces new findings on the combination of these techniques, which are presented in the conclusions.…”

“…Deep Learning models learn directly from data and require large datasets to obtain good accuracies. To avoid the latter, some techniques have been well proven to obtain models with better results, such as Data Augmentation [17], [18], [19], Transfer Learning [20], [21], [22], and Fine-Tuning [23], [24]. But there is no study on the impact of each and combination of these techniques.…”