Multimodal feature fusion in deep learning for comprehensive dental condition classification.

IF 1.7 3区 医学 Q3 INSTRUMENTS & INSTRUMENTATION Journal of X-Ray Science and Technology Pub Date : 2024-01-01 DOI:10.3233/XST-230271
Shang-Ting Hsieh, Ya-Ai Cheng
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Abstract

Background: Dental health issues are on the rise, necessitating prompt and precise diagnosis. Automated dental condition classification can support this need.

Objective: The study aims to evaluate the effectiveness of deep learning methods and multimodal feature fusion techniques in advancing the field of automated dental condition classification.

Methods and materials: A dataset of 11,653 clinically sourced images representing six prevalent dental conditions-caries, calculus, gingivitis, tooth discoloration, ulcers, and hypodontia-was utilized. Features were extracted using five Convolutional Neural Network (CNN) models, then fused into a matrix. Classification models were constructed using Support Vector Machines (SVM) and Naive Bayes classifiers. Evaluation metrics included accuracy, recall rate, precision, and Kappa index.

Results: The SVM classifier integrated with feature fusion demonstrated superior performance with a Kappa index of 0.909 and accuracy of 0.925. This significantly surpassed individual CNN models such as EfficientNetB0, which achieved a Kappa of 0.814 and accuracy of 0.847.

Conclusions: The amalgamation of feature fusion with advanced machine learning algorithms can significantly bolster the precision and robustness of dental condition classification systems. Such a method presents a valuable tool for dental professionals, facilitating enhanced diagnostic accuracy and subsequently improved patient outcomes.

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深度学习中的多模态特征融合,用于综合牙科状况分类。
背景:牙科健康问题日益增多,需要及时准确的诊断。自动牙科状况分类可满足这一需求:本研究旨在评估深度学习方法和多模态特征融合技术在推进牙科状况自动分类领域的有效性:该数据集包含 11653 张临床图片,代表了六种常见的牙科疾病--龋齿、牙结石、牙龈炎、牙齿变色、溃疡和牙髓发育不全。使用五个卷积神经网络(CNN)模型提取特征,然后融合成矩阵。使用支持向量机(SVM)和奈夫贝叶斯分类器构建了分类模型。评估指标包括准确率、召回率、精确度和 Kappa 指数:结果:与特征融合集成的 SVM 分类器表现优异,Kappa 指数为 0.909,精确度为 0.925。这大大超过了单独的 CNN 模型,如 EfficientNetB0,其 Kappa 指数为 0.814,准确率为 0.847:将特征融合与先进的机器学习算法相结合,可以大大提高牙科状况分类系统的精确度和稳健性。这种方法为牙科专业人员提供了宝贵的工具,有助于提高诊断准确性,进而改善患者的治疗效果。
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来源期刊
CiteScore
4.90
自引率
23.30%
发文量
150
审稿时长
3 months
期刊介绍: Research areas within the scope of the journal include: Interaction of x-rays with matter: x-ray phenomena, biological effects of radiation, radiation safety and optical constants X-ray sources: x-rays from synchrotrons, x-ray lasers, plasmas, and other sources, conventional or unconventional Optical elements: grazing incidence optics, multilayer mirrors, zone plates, gratings, other diffraction optics Optical instruments: interferometers, spectrometers, microscopes, telescopes, microprobes
期刊最新文献
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