Abstract
Humans frequently experience dental problems, and as the population consumes more sugar and sweets, these problems will become more prevalent. The dentist always locates the problems through physical examination and X-ray photos. Technology is advancing quickly across the board in the health sciences, and deep learning modules known as transfer learning are highly helpful in recognizing patterns or individual pixels in an imagenet. Dental X-ray pictures can be employed in the transfer learning process as well as the CNN approach. In this study, dental X-ray pictures are recognized using six transfer learning models: Resnet50, VGG16, InceptionV3, Xception, and EfficientnetB7. Although the InceptionV3 also offers the best waiting time of 7.58 min with an accuracy of 0.93, the Densenet201 offers the best accuracy of 0.98 with a waiting time of 12.91 min. Although the waiting time is longer than inceptionV3, it can be argued that dental abnormalities can be diagnosed more effectively with the densenet201.
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Meem, F.A., Ferdus, J., Sarkar, W.A., Ahmed, M.I., Islam, M.S. (2023). Detection of Dental Issues Using the Transfer Learning Methods. In: Kaiser, M.S., Waheed, S., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds) Proceedings of the Fourth International Conference on Trends in Computational and Cognitive Engineering. Lecture Notes in Networks and Systems, vol 618. Springer, Singapore. https://doi.org/10.1007/978-981-19-9483-8_31
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