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

被引：0

作者：

Inani H. ^{[1
]}

Mehta V. ^{[1
]}

Bhavsar D. ^{[1
]}

Gupta R.K. ^{[1
]}

Jain A. ^{[2
]}

Akhtar Z. ^{[3
]}

机构：

[1] Department of Computer Science and Engineering, Pandit Deendayal Energy University, Gujarat, Gandhinagar

[2] Department of Computer Science and Engineering and Information Technology, Jaypee Institute of Information Technology, Uttar Pradesh, Noida

[3] Department of Network and Computer Security, State University of New York Polytechnic Institute, NY

来源：

Journal of Ambient Intelligence and Humanized Computing | 2024年 / 15卷 / 07期

关键词：

Dental caries detection; EfficientNetV2B3; Grad-CAM; ImageDataGenerator; InceptionResNetV2; Pre-trained architectures; ResNet50; Transfer learning; VGG19; Xception;

D O I：

10.1007/s12652-024-04795-x

中图分类号：

学科分类号：

摘要：

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. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

引用

页码：3009 / 3033

页数：24

共 42 条

[1]

Aayush J., Tiwari H., Singh U., Kumar N., Kumar S., Dental caries detection using faster R-CNN and YOLO V3, ITM Web Conf, 53, (2023)

[2]

Aeini F., Mahmoudi F., Classification and numbering of posterior teeth in bitewing dental images, In: 2010 3Rd International Conference on Advanced Computer Theory and Engineering (ICACTE, (2010)

[3]

AlSayyed A., Taqateq A., Al-Sayyed R., Suleiman D., Shukri S., Alhenawi E., Albsheish A., Employing CNN ensemble models in classifying dental caries using oral photographs, Int J Data Netw Sci, 7, 4, pp. 1535-1550, (2023)

[4]

Anny Y., Nugroho A.S., Amaliah B., Arifin A.Z., Classification and numbering of dental radiographs for an automated human identification system, TELKOMNIKA Telecommun Comput Electron Control, 10, (2012)

[5]

Balasubramaniam S., Vijesh Joe C., Sivakumar T.A., Prasanth A., Satheesh Kumar K., Kavitha V., Dhanaraj R.K., Optimization enabled deep learning-based DDoS attack detection in cloud computing, Int J Intell Syst, 2023, (2023)

[6]

Braveen M., Nachiyappan S., Seetha R., Anusha K., Ahilan A., Prasanth A., Jeyam A., ALBAE feature extraction based lung pneumonia and cancer classification, Soft Comput, 16, pp. 1-14, (2023)

[7]

Chen D.S., Yang C.-M., Chen M.-J., Chen M.-C., Weng R.-M., Yeh C.-H., Deep learning-based recognition of periodontitis and dental caries in dental X-ray images, Bioengineering, 10, 8, (2023)

[8]

Devi D.S.R., Kostova N.M., Cancer medicine: a missed opportunity, Lancet, 395, pp. 1257-1258, (2020)

[9]

Haghanifar A., Majdabadi N.M., Ko S.B., Haghanifar S., Choi Y., Ko S.B., PaXNet: Tooth segmentation and dental caries detection in panoramic X-ray using ensemble transfer learning and capsule classifier, Multimedia Tools Appl, 82, pp. 1-21, (2023)

[10]

Heck K., Kunzelmann K.-H., Walter E., Kaisarly D., Hoffmann L., Litzenburger F., Proximal caries detection using short-wave infrared transillumination at wavelengths of 1050, 1200 and 1300 nm in permanent posterior human teeth, Diagnostics, 13, 20, (2023)

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