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

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: International Journal of Advanced Computer Science and Applications

سال: 2022

ISSN: ['2158-107X', '2156-5570']

DOI: https://doi.org/10.14569/ijacsa.2022.0131230