An Adaptive Enhancement Based Hybrid CNN Model for Digital Dental X-ray Positions Classification

Wang, Yaqi; Sun, Lingling; Yi-fang, Zhang; Lv, Dailin; Li, Zhixing; Qi, Wuteng

doi:10.48550/arxiv.2005.01509

Cited by 1 publication

(4 citation statements)

References 9 publications

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“…Shifting the focus to 2020, the dataset in the study [14] conducted by scholars from the Microelectronics CAD Center at Hangzhou Dianzi University, Hangzhou, China, and the West China School of Stomatology at Sichuan University, comprised 2491 dental Xrays for model training purposes and 138 X-rays for the validation process. The dental radiographs were sorted into six distinct groups, each corresponding to different sequences of tooth numbering.…”

Section: Datasets Used For Ai-based Systems For Dentistry E-healthmentioning

confidence: 99%

“…It automatically finds efficient neural network architectures. When combined with Inception V4, it enhances classification performance by generating feature vectors from both networks and using a two-layer fully connected network for final classification [14]. Figure 5 shows a mixed model of the Inception V4 + NASNetMobile structure.…”

Section: Nasnetmobilementioning

confidence: 99%

“…Moving forward to the subsequent year, 2020, an automatic dental X-ray detection technique using a hybrid multi-convolution neural network (CNN) and adaptive histogram equalization was presented by Yaqi Wang et al [14]. Achieving good accuracy, specificity, and AUC, the study indicated a noteworthy step forward, but with a lack of explicit neural network architecture specifications.…”

Section: Relevant Work Experiencesmentioning

confidence: 99%

“…Moving to 2020, Yaqi Wang et al [14] introduced an automated dental X-ray detection methodology utilizing adaptive histogram equalization and a hybrid multi-convolutional neural network (CNN). This innovative approach addressed the critical role of dental radiograph analysis in clinical diagnostics, where expert interpretation includes tasks such as teeth detection and numbering.…”

Section: Role Of X-ray Imaging In Dental Diagnosticsmentioning

confidence: 99%

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Advancing Dental Diagnostics: A Review of Artificial Intelligence Applications and Challenges in Dentistry

Musleh,

Almossaeed,

Balhareth

et al. 2024

BDCC

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The rise of artificial intelligence has created and facilitated numerous everyday tasks in a variety of industries, including dentistry. Dentists have utilized X-rays for diagnosing patients’ ailments for many years. However, the procedure is typically performed manually, which can be challenging and time-consuming for non-specialized specialists and carries a significant risk of error. As a result, researchers have turned to machine and deep learning modeling approaches to precisely identify dental disorders using X-ray pictures. This review is motivated by the need to address these challenges and to explore the potential of AI to enhance diagnostic accuracy, efficiency, and reliability in dental practice. Although artificial intelligence is frequently employed in dentistry, the approaches’ outcomes are still influenced by aspects such as dataset availability and quantity, chapter balance, and data interpretation capability. Consequently, it is critical to work with the research community to address these issues in order to identify the most effective approaches for use in ongoing investigations. This article, which is based on a literature review, provides a concise summary of the diagnosis process using X-ray imaging systems, offers a thorough understanding of the difficulties that dental researchers face, and presents an amalgamative evaluation of the performances and methodologies assessed using publicly available benchmarks.

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Section: Datasets Used For Ai-based Systems For Dentistry E-healthmentioning

confidence: 99%

Section: Nasnetmobilementioning

confidence: 99%

Section: Relevant Work Experiencesmentioning

confidence: 99%

Section: Role Of X-ray Imaging In Dental Diagnosticsmentioning

confidence: 99%

See 2 more Smart Citations