Empowering Maxillofacial Diagnosis Through Transfer Learning Models

Abstract

The purpose of this research is to better diagnose maxillofacial fractures by using transfer learning models such as AlexNet, Vgg16, ResNet50, and a suggested CNN architecture. Multiple forms and locations of maxillofacial fractures are shown by the photos used. The maxillofacial fracture dataset is used to fine-tune the transfer learning models that have been pretrained on large-scale natural image datasets. The results of experimental assessments show that transfer learning models outperform conventional approaches. The suggested CNN architecture outperforms the state-of-the-art models in terms of accuracy, precision, and recall (f1 score). Differential analyses reveal that AlexNet is best at seeing skin-deep fractures, Vgg16 is adept at catching the finer points of complicated fractures, and ResNet50 excels at spotting fractures that span numerous body parts. The suggested CNN design demonstrates flexibility in terms of fracture kind and anatomical location. Accurate and efficient fracture identification may be obtained by using pretrained models and proposing fine-tuning on particular medical imaging datasets. Improving treatment results and patient care, this study adds to the creation of cutting-edge diagnostic tools for maxillofacial fractures.

Ähnliche Arbeiten