AI-enabled dental caries detection using transfer learning and gradient-based class activation mapping

3区 计算机科学 Q1 Computer Science Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-21 DOI:10.1007/s12652-024-04795-x
Hardik Inani, Veerangi Mehta, Drashti Bhavsar, Rajeev Kumar Gupta, Arti Jain, Zahid Akhtar
{"title":"AI-enabled dental caries detection using transfer learning and gradient-based class activation mapping","authors":"Hardik Inani, Veerangi Mehta, Drashti Bhavsar, Rajeev Kumar Gupta, Arti Jain, Zahid Akhtar","doi":"10.1007/s12652-024-04795-x","DOIUrl":null,"url":null,"abstract":"<p>Dental caries detection holds the key to unlocking brighter smiles and healthier lives by identifying one of the most common oral health issues early on. This vital topic sheds light on innovative ways to combat tooth decay, empowering individuals to take control of their oral health and maintain radiant smiles. This research paper delves into the realm of transfer learning techniques, aiming to elevate the precision and efficacy of dental caries diagnosis. Utilizing Keras ImageDataGenerator, a rich and balanced dataset is crafted by augmenting teeth images from the Kaggle teeth dataset. Five cutting-edge pre-trained architectures are harnessed in the transfer learning approach: EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50, with each model, initialized using ImageNet weights and tailored top layers. A comprehensive set of evaluation metrics, encompassing accuracy, precision, recall, F1-score, and false negative rates are employed to gauge the performance of these architectures. The findings unveil the unique advantages and drawbacks of each model, illuminating the path to an optimal choice for dental caries detection using Grad-CAM (Gradient-weighted Class Activation Mapping). The testing accuracies achieved by EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models stand at 95.89%, 96.58%, 93.15%, 93.15%, and 94.18%, respectively. The Training accuracies stood at 100%, 99.91%, 100%, 100% and 100%, meanwhile on validation we achieved 97.63%, 96.68%, 98.82%, 96.68%, and 100% accuracies for EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models respectively. Capitalizing on transfer learning and juxtaposing diverse pre-trained architectures, this research paper paves the way for substantial advancements in dental diagnostic capabilities, culminating in enhanced patient outcomes and superior oral health.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"4 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Ambient Intelligence and Humanized Computing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s12652-024-04795-x","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0

Abstract

Dental caries detection holds the key to unlocking brighter smiles and healthier lives by identifying one of the most common oral health issues early on. This vital topic sheds light on innovative ways to combat tooth decay, empowering individuals to take control of their oral health and maintain radiant smiles. This research paper delves into the realm of transfer learning techniques, aiming to elevate the precision and efficacy of dental caries diagnosis. Utilizing Keras ImageDataGenerator, a rich and balanced dataset is crafted by augmenting teeth images from the Kaggle teeth dataset. Five cutting-edge pre-trained architectures are harnessed in the transfer learning approach: EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50, with each model, initialized using ImageNet weights and tailored top layers. A comprehensive set of evaluation metrics, encompassing accuracy, precision, recall, F1-score, and false negative rates are employed to gauge the performance of these architectures. The findings unveil the unique advantages and drawbacks of each model, illuminating the path to an optimal choice for dental caries detection using Grad-CAM (Gradient-weighted Class Activation Mapping). The testing accuracies achieved by EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models stand at 95.89%, 96.58%, 93.15%, 93.15%, and 94.18%, respectively. The Training accuracies stood at 100%, 99.91%, 100%, 100% and 100%, meanwhile on validation we achieved 97.63%, 96.68%, 98.82%, 96.68%, and 100% accuracies for EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models respectively. Capitalizing on transfer learning and juxtaposing diverse pre-trained architectures, this research paper paves the way for substantial advancements in dental diagnostic capabilities, culminating in enhanced patient outcomes and superior oral health.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用迁移学习和基于梯度的类激活映射进行人工智能龋齿检测
龋齿检测是开启灿烂笑容和健康生活的钥匙,它能及早发现最常见的口腔健康问题之一。这一重要课题揭示了防治蛀牙的创新方法,使人们有能力控制自己的口腔健康,保持灿烂的笑容。本研究论文深入探讨了迁移学习技术领域,旨在提高龋齿诊断的精确性和有效性。利用 Keras ImageDataGenerator,通过增强来自 Kaggle 牙齿数据集的牙齿图像,制作了一个丰富而均衡的数据集。在迁移学习方法中利用了五种前沿的预训练架构:EfficientNetV2B3、VGG19、InceptionResNetV2、Xception 和 ResNet50,每个模型都使用 ImageNet 权重和定制的顶层进行初始化。为了衡量这些架构的性能,我们采用了一套全面的评估指标,包括准确率、精确度、召回率、F1-分数和假负率。研究结果揭示了每种模型的独特优势和缺点,为使用 Grad-CAM(梯度加权类激活映射)进行龋齿检测提供了最佳选择。EfficientNetV2B3、VGG19、InceptionResNetV2、Xception 和 ResNet50 模型的测试准确率分别为 95.89%、96.58%、93.15%、93.15% 和 94.18%。训练准确率分别为 100%、99.91%、100%、100% 和 100%,而在验证时,EfficientNetV2B3、VGG19、InceptionResNetV2、Xception 和 ResNet50 模型的准确率分别为 97.63%、96.68%、98.82%、96.68% 和 100%。本研究论文利用迁移学习和并置不同的预训练架构,为牙科诊断能力的大幅提升铺平了道路,最终提高了患者的治疗效果和口腔健康水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Ambient Intelligence and Humanized Computing
Journal of Ambient Intelligence and Humanized Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.60
自引率
0.00%
发文量
854
期刊介绍: The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to): Pervasive/Ubiquitous Computing and Applications Cognitive wireless sensor network Embedded Systems and Software Mobile Computing and Wireless Communications Next Generation Multimedia Systems Security, Privacy and Trust Service and Semantic Computing Advanced Networking Architectures Dependable, Reliable and Autonomic Computing Embedded Smart Agents Context awareness, social sensing and inference Multi modal interaction design Ergonomics and product prototyping Intelligent and self-organizing transportation networks & services Healthcare Systems Virtual Humans & Virtual Worlds Wearables sensors and actuators
期刊最新文献
Predicting the unconfined compressive strength of stabilized soil using random forest coupled with meta-heuristic algorithms Expressive sign language system for deaf kids with MPEG-4 approach of virtual human character MEDCO: an efficient protocol for data compression in wireless body sensor network A multi-objective gene selection for cancer diagnosis using particle swarm optimization and mutual information Partial policy hidden medical data access control method based on CP-ABE
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1