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Muhammad Adnan Hasnain
Muhammad Ashad Baloch
Aqsa Jameel
Zaid Sarfraz
Abdul Majid Soomro
Imran Khurshid


Deep Learning, Dental Disease, Tooth Decay, Disease Detection, Computer Vision


Dental problems now affect a sizable section of the population and are a common global health problem. It is essential to get an early and precise diagnosis of dental disorders in order to treat them effectively and avoid subsequent consequences. Deep learning algorithms have recently demonstrated astounding effectiveness in a variety of health imaging claims. Through the use of dental radiographs, this study intends to investigate the potential of deep learning for the classification of dental illnesses.

A dataset with a variety of dental radiographs was gathered, including both healthy teeth and those with effected dental. Utilising dental radiographs as a source, convolutional neural networks (CNNs) were used to extract distinguishing features. To assess how well different CNN architectures performed in classifying dental diseases, popular models like VGGNet19, ResNet50, and DenseNet169 were used.

The outcomes showed that deep learning models were effective at classifying dental diseases. The top-performing model outperformed on conventional machine learning methods and had a classification accuracy of over 99.90%. The models were effective at distinguishing between various dental disorders, such as Healthy and effected teeth. The models also demonstrated good specificity and sensitivity, recall, precision, f1score and training testing accuracy highlighting their potential as trustworthy diagnostic tools.

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