ArticleComputational intelligence and neuroscience2022
Detection of Dental Diseases through X-Ray Images Using Neural Search Architecture Network.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 53 citations in OpenAlex.
- Advanced Deep Learning Models for Classifying Dental Diseases from Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Improving dental disease diagnosis using a cross attention based hybrid model of DeiT and CoAtNet.Scientific reports · 2026Article
- Multi-label diagnosis of dental conditions from panoramic x-rays using attention-enhanced deep learning.Oral and maxillofacial surgery · 2025Article
- Development of Periapical Index Score Classification System in Periapical Radiographs Using Deep Learning.Journal of imaging informatics in medicine · 2025Article
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- Multi-label dental disorder diagnosis based on MobileNetV2 and swin transformer using bagging ensemble classifier.Scientific reports · 2024Article
- Role of Artificial Intelligence (AI) in Patient Education and Communication in Dentistry.Cureus · 2024Review
- Artificial Intelligence Based COVID-19 Detection and Classification Model on Chest X-ray Images.Healthcare (Basel, Switzerland) · 2023Article
- Enhancing prediction of tooth caries using significant features and multi-model classifier.PeerJ. Computer science · 2023Article
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An important aspect of the diagnosis procedure in daily clinical practice is the analysis of dental radiographs. This is because the dentist must interpret different types of problems related to teeth, including the tooth numbers and related diseases during the diagnostic process. For panoramic radiographs, this paper proposes a convolutional neural network (CNN) that can do multitask classification by classifying the X-ray images into three classes: cavity, filling, and implant. In this paper, convolutional neural networks are taken in the form of a NASNet model consisting of different numbers of max-pooling layers, dropout layers, and activation functions. Initially, the data will be augmented and preprocessed, and then, the construction of a multioutput model will be done. Finally, the model will compile and train the model; the evaluation parameters used for the analysis of the model are loss and the accuracy curves. The model has achieved an accuracy of greater than 96% such that it has outperformed other existing algorithms.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.