PaXNet: Dental Caries Detection in Panoramic X-ray using Ensemble Transfer Learning and Capsule Classifier

• URL: http://arxiv.org/abs/2012.13666v1
• Date: Sat, 26 Dec 2020 03:00:35 GMT
• Title: PaXNet: Dental Caries Detection in Panoramic X-ray using Ensemble Transfer Learning and Capsule Classifier
• Authors: Arman Haghanifar, Mahdiyar Molahasani Majdabadi, Seok-Bum Ko
• Abstract summary: In many cases, dental caries is hard to identify using x-rays due to different reasons such as low image quality. Here, we propose an automatic diagnosis system to detect dental caries in Panoramic images for the first time. The proposed model benefits from various pretrained deep learning models through transfer learning to extract relevant features from x-rays and uses a capsule network to draw prediction results.
• Score: 8.164433158925593
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Dental caries is one of the most chronic diseases involving the majority of the population during their lifetime. Caries lesions are typically diagnosed by radiologists relying only on their visual inspection to detect via dental x-rays. In many cases, dental caries is hard to identify using x-rays and can be misinterpreted as shadows due to different reasons such as low image quality. Hence, developing a decision support system for caries detection has been a topic of interest in recent years. Here, we propose an automatic diagnosis system to detect dental caries in Panoramic images for the first time, to the best of authors' knowledge. The proposed model benefits from various pretrained deep learning models through transfer learning to extract relevant features from x-rays and uses a capsule network to draw prediction results. On a dataset of 470 Panoramic images used for features extraction, including 240 labeled images for classification, our model achieved an accuracy score of 86.05\% on the test set. The obtained score demonstrates acceptable detection performance and an increase in caries detection speed, as long as the challenges of using Panoramic x-rays of real patients are taken into account. Among images with caries lesions in the test set, our model acquired recall scores of 69.44\% and 90.52\% for mild and severe ones, confirming the fact that severe caries spots are more straightforward to detect and efficient mild caries detection needs a more robust and larger dataset. Considering the novelty of current research study as using Panoramic images, this work is a step towards developing a fully automated efficient decision support system to assist domain experts.

Related papers

• Semi-supervised classification of dental conditions in panoramic radiographs using large language model and instance segmentation: A real-world dataset evaluation [6.041146512190833]
  A semi-supervised learning framework is proposed to classify thirteen dental conditions on panoramic radiographs. The solution demonstrated an accuracy level comparable to that of a junior specialist.
  arXiv  Detail & Related papers  (2024-06-25T19:56:12Z)
• Intergrated Segmentation and Detection Models for Dentex Challenge 2023 [2.1025078609239403]
  With the development of deep learning, auto detection of diseases from dental panoramic x-rays can help dentists to diagnose diseases more efficiently. The Dentex Challenge 2023 is a competition for automatic detection of abnormal teeth along with their enumeration ids from dental panoramic x-rays.
  arXiv  Detail & Related papers  (2023-08-27T17:44:25Z)
• Dental CLAIRES: Contrastive LAnguage Image REtrieval Search for Dental Research [6.628588447133907]
  The proposed framework, Contrastive LAnguage Image REtrieval Search for dental research, Dental CLAIRES, retrieves the best-matched images based on the text query. Our model achieved a hit@3 ratio of 96% and a Mean Reciprocal Rank (MRR) of 0.82.
  arXiv  Detail & Related papers  (2023-06-27T17:47:12Z)
• Self-Supervised Learning with Masked Image Modeling for Teeth Numbering, Detection of Dental Restorations, and Instance Segmentation in Dental Panoramic Radiographs [8.397847537464534]
  This study aims to utilize recent self-supervised learning methods like SimMIM and UM-MAE to increase the model efficiency and understanding of the limited number of dental radiographs. To the best of our knowledge, this is the first study that applied self-supervised learning methods to Swin Transformer on dental panoramic radiographs.
  arXiv  Detail & Related papers  (2022-10-20T16:50:07Z)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• OdontoAI: A human-in-the-loop labeled data set and an online platform to boost research on dental panoramic radiographs [53.67409169790872]
  This study addresses the construction of a public data set of dental panoramic radiographs. We benefit from the human-in-the-loop (HITL) concept to expedite the labeling procedure. Results demonstrate a 51% labeling time reduction using HITL, saving us more than 390 continuous working hours.
  arXiv  Detail & Related papers  (2022-03-29T18:57:23Z)
• Generative Residual Attention Network for Disease Detection [51.60842580044539]
  We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning. We generate a corresponding radiology image in a target domain while preserving the identity of the patient. We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
  arXiv  Detail & Related papers  (2021-10-25T14:15:57Z)
• Cross-Modal Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop [63.81818077092879]
  We propose an end-to-end semi-supervised cross-modal contrastive learning framework for medical images. We first apply an image encoder to classify the chest X-rays and to generate the image features. The radiomic features are then passed through another dedicated encoder to act as the positive sample for the image features generated from the same chest X-ray.
  arXiv  Detail & Related papers  (2021-04-11T09:16:29Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification [49.32653090178743]
  We demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning. Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods.
  arXiv  Detail & Related papers  (2020-07-02T12:00:53Z)
• Individual Tooth Detection and Identification from Dental Panoramic X-Ray Images via Point-wise Localization and Distance Regularization [10.877276642014515]
  The proposed network initially performs center point regression for all the anatomical teeth, which automatically identifies each tooth. Teeth boxes are individually localized using a cascaded neural network on a patch basis. The experimental results demonstrate that the proposed algorithm outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-04-12T04:14:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.