Classification of dental diseases using CNN and transfer learning

Prajapati, Shreyansh A.; Nagaraj, R.; Mitra, Suman K.

doi:10.1109/iscbi.2017.8053547

Cited by 110 publications

(48 citation statements)

References 13 publications

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“…A total of 251 radiovisiography images were employed by Prajapati et al to detect caries with a convolutional neural network, which achieved an accuracy of 0.875 [29].…”

Section: Caries Detectionmentioning

confidence: 99%

Dental Images Recognition Technology and Applications: A Literature Review

Prados-Privado

Villalón²,

Martínez-Martínez³

et al. 2020

Applied Sciences

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Neural networks are increasingly being used in the field of dentistry. The aim of this literature review was to visualize the state of the art of artificial intelligence in dental applications, such as the detection of teeth, caries, filled teeth, crown, prosthesis, dental implants and endodontic treatment. A search was conducted in PubMed, the Institute of Electrical and Electronics Engineers (IEEE) Xplore and arXiv.org. Data extraction was performed independently by two reviewers. Eighteen studies were included. The variable teeth was the most analyzed (n = 9), followed by caries (n = 7). No studies detecting dental implants and filled teeth were found. Only two studies investigated endodontic applications. Panoramic radiographies were the most common image employed (n = 5), followed by periapical images (n = 3). Near-infrared light transillumination images were employed in two studies and bitewing and computed tomography (CT) were employed in one study. The included articles used a wide variety of neuronal networks to detect the described variables. In addition, the database used also had a great heterogeneity in the number of images. A standardized methodology should be used in order to increase the compatibility and robustness between studies because of the heterogeneity in the image database, type, neural architecture and results.

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“…A total of 251 radiovisiography images were employed by Prajapati et al to detect caries with a convolutional neural network, which achieved an accuracy of 0.875 [29].…”

Section: Caries Detectionmentioning

confidence: 99%

Dental Images Recognition Technology and Applications: A Literature Review

Prados-Privado

Villalón²,

Martínez-Martínez³

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Kavitha et al [22] employed support vector machines to diagnose osteoporosis from dental panoramic radiographs. Lately, Prajapati et al [28] developed a VGG16-based Convolutional Neural Networks (CNN) to identify dental caries, periapical infection and periodontitis. Another deep network [21] was recently trained on optical coherence tomography images to classify human oral tissues and detect dental caries.…”

Section: Related Workmentioning

confidence: 99%

A First Glance to the Quality Assessment of Dental Photostimulable Phosphor Plates with Deep Learning

Ariana

Calderón-Ramírez

Thang

et al. 2020

2020 International Joint Conference on Neural Networks (IJCNN)

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Photostimulable Phosphor Plates are commonly used in digital X-ray imaging for dentistry. During its usage, these plates get damaged, influencing the diagnosis performance and confidence of the dentistry professional. We propose a deep learning based classifier to discard or extend the use of photostimulable phosphor (PSP) plates based on their physical damage. The system automatically assesses, for the first time in the literature, when dentists should discard their plates. To validate our methodology, an in-house dataset is built on 25 PSP artifact masks (Carestream, CS 7600) digitally superimposed over 100 Complementary Metal-oxide-semiconductor (CMOS) periapical images (Carestream, RVG 6200) with known radiologic interpretations. From these 2500 images, unique subsets of 100 images were evaluated by 25 dentists to find periapical inflammatory lesions on the tooth. Doctors' opinion on whether the plates should be discarded or not was also collected. Stateof-the-art deep convolutional networks were tested using fivefold cross validation, yielding classification accuracies from 87% to almost 89%. Specifically, InceptionV3 and Resnet50 obtained the best performances with statistical significance. Qualitative heat-maps showed that such models can identify and employ artifacts to decide on whether to discard the PSP plate or not.

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“…For x-ray images, small labeled dental dataset with three different CNN architectures was exploited for diagnosis classification. 18 G. Next et al 19 explored the transferred Inception V3 model pre-trained on the ImageNet dataset and fine-tuned it with labeled endoscopy images for gastrointestinal bleeding detection. As for ultrasound images, 20 transfer learning based on VGG-Net model and a new designed FCNet were used to classify liver fibrosis.…”

Section: Deep Transfer Learning In Medical Image Classificationmentioning

confidence: 99%

Lymph node detection method based on multisource transfer learning and convolutional neural network

Peng

2019

Int J Imaging Syst Tech

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Recently years, convolutional neural networks (CNNs) have proven to be powerful tools for a broad range of computer vision tasks. However, training a CNN from scratch is difficult because it requires a large amount of labeled training data, which remains a challenge in medical imaging domain. To this end, deep transfer learning (TL) technique is widely used for many medical image tasks. In this paper, we propose a novel multisource transfer learning CNN model for lymph node detection.The mechanism behind it is straightforward. Point-wise (1 × 1) convolution is used to fuse multisource transfer learning knowledge. Concretely, we view the transferred features as priori domain knowledge and 1 × 1 convolutional operation is implemented after pre-trained convolution layers to adaptively combine the transfer information for target task. In order to learn non-linear transferred features and prevent over-fitting, we present an encode process for the pre-trained convolution kernels. At last, based on convolutional factorization technique, we train the proposed CNN model and the encoder process jointly, which improves the feasibility of our approach. The effectiveness of the proposed method is verified on lymph node (LN) dataset: 388 mediastinal LNs labeled by radiologists in 90 patient CT scans, and 595 abdominal LNs in 86 patient CT scans for LN detection. Our method demonstrates sensitivities of about 85%/71% at 3 FP/vol. and 92%/85% at 6 FP/vol. for mediastinum and abdomen respectively, which compares favorably to previous methods. K E Y W O R D S convolutional neural network, lymph node detection, multisource transfer learning, point-wise convolutional operation

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Classification of dental diseases using CNN and transfer learning

Cited by 110 publications

References 13 publications

Dental Images Recognition Technology and Applications: A Literature Review

Dental Images Recognition Technology and Applications: A Literature Review

A First Glance to the Quality Assessment of Dental Photostimulable Phosphor Plates with Deep Learning

Lymph node detection method based on multisource transfer learning and convolutional neural network

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