The document describes an object detection model using region-based convolutional neural networks to detect dental caries and root canals in dental x-ray images. The model was trained on a dataset of dental x-rays labeled with caries and root canal objects. Feature extraction was performed using multiple convolutional layers and concatenation. Anchors, multi-scale training, and hard negative mining were used to improve performance. The model achieved 83.45% accuracy for object detection, outperforming other methods. Future work could include working with larger datasets and real-time detection from video.
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Object Detection on Dental X-rays using Region Based CNNs
1. Object Detection on Dental X-ray
Images using Region Based
Convolutional Neural Networks
Rakib Hossen, Minhazul Arefin and Mohammed Nasir Uddin
INTERNATIONAL CONFERENCE ON
MACHINE INTELLIGENCE & DATA
SCIENCE APPLICATIONS
(MIDAS 2021)
Date: December 26-27, 2021
3. Introduction
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Object detection is a computer vision technique that
allows us to identify and locate objects in an image or
video.
Tooth decay, also known as dental caries or cavities,
is the breakdown of teeth due to acids made by
bacteria.
Root canal is a dental procedure involving the removal
of the soft center of the tooth, the pulp.
4. Introduction
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In 2020, the WHO estimated that about 50% of the world
population is affected by dental caries.
In some Asian-Pacific countries, the incidence of oral cancer is
within the top 3 of all cancers.
Dental caries are cavities or holes (a type of structural damage)
in the teeth.
Root canal causes due to inflammation or infection in the roots of
a tooth.
5. Problem Statement
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Manual feature extraction for the detection of caries and root
canal
Difficulties in generating hierarchical features
Inaccurate Region Of Interest (ROI) detection and edge
detection
Difficulties in training model with good convergence
6. Objectives
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To develop a framework that extracts features efficiently
To design an efficient deep learning approach that can
aid in the automatic detection of objects in dental X-rays
To classify the detected objects in the dental x-ray
images into caries and root canal.
7. Contributions
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Using state-of-the-art model
Parameterizing the four co-ordinates of the detected
object in the multi scale training
Using 12 anchors in the RPN
9. Image Acquisition
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Source: Digital Dental Periapical X-Ray Dataset
Two categories: Caries and Root canal
Type: Dental Periapical images
80% for training and 20% for testing
10. Model pre-training
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Pre-train the model on COCO dataset
Fine tune the model
Train the model on Digital Dental Periapical
X-Ray Dataset
Fine tune the model again
11. Hard negative mining
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Boosts performance especially object detection
Hard negatives are the regions where the network has failed
to make correct prediction
Fed into the network again as a reinforcement for improving
trained model
Towards fewer false positives and better classification
performance
12. Number of anchors
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Smaller items such as implants seem to be quite frequent in object
detection tasks
We uses two conditions to assign a positive label to an anchor
𝐿(𝑝𝑖, 𝑡𝑖) =
1
𝑁𝑐𝑙𝑠
𝑖
𝐿𝑐𝑙𝑠 𝑝𝑖, 𝑝𝑖
∗
+ 𝜆
1
𝑁𝑟𝑒𝑔
𝑖
𝑝𝑖
∗
𝐿𝑟𝑒𝑔 𝑡𝑖, 𝑡𝑖
∗
A mini-batch has an anchor 𝑖, and 𝑝𝑖 is a projected probability that
anchor 𝑖 will be a real item
13. Number of anchors
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Most Crucial Part in RPN
Traditional Faster R-CNN uses 9 anchors
Sometimes fails to detect smaller object
In this study we used 12 anchors
14. Multi-scale training
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Resizes the images to a random scale
The detector will be able to learn features across a wide range of sizes
Improves the performance towards scale invariance
We compute the bounding box regression by parameterized the four
co-ordinates of the detected object. It is shown in equation:
𝑡𝑝 = (𝑝 − 𝑝𝑎)/𝑟𝑎, 𝑡𝑞 = (𝑞 − 𝑞𝑎)/𝑠𝑎
𝑡𝑟 = 𝑙𝑜𝑔
𝑟
𝑟𝑎
, 𝑡𝑠 = 𝑙𝑜𝑔
𝑠
𝑠𝑎
𝑡𝑝
∗
=
𝑝∗
− 𝑝𝑎
𝑟𝑎
, 𝑡𝑞
∗
=
𝑞∗
− 𝑞𝑎
𝑠𝑎
𝑡𝑟
∗
= 𝑙𝑜𝑔
𝑟∗
𝑟𝑎
, 𝑡𝑠
∗
= 𝑙𝑜𝑔
𝑠∗
𝑠𝑎
15. Feature Concatenation
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Feature concatenation is an effective way to add different features
together to enhance the classification process.
Features are RoI-pooled and L2-normalized from several lower-level
convolution layers accordingly.
These characteristics are then concatenated and rescaled as if the
original scale of the features had not been adopted.
A 1×1 convolution is done to match the original network’s number of
channels.
16. Feature Concatenation
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Features from multiple convolutional layers
Convolutional layers: lower level & higher level
Features: ROI pooled & L2 normalized
17. Object Detection
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Assign some labels to an object based on their
features using soft-max classifier
Detect objects in dental x-rays image
successfully
18. Result
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The overlap between two borders is measured by the IoU.
The overlap between our anticipated border and the ground reality is
then calculated (the real object boundary).
Let 𝑛𝑖,𝑗 be the number of pixels of class 𝑖 predicted to belong to class 𝑗,
where there are 𝑛𝑐𝑙 different classes, and let 𝑡𝑖 = 𝑗 𝑛𝑖𝑗 be the total
number of pixels of class 𝑖.
Mean IoU is defined as:
𝑀𝑒𝑎𝑛𝐼𝑜𝑈 =
1
𝑛𝑐𝑙
𝑖
𝑛𝑖𝑖
𝑡𝑖 + 𝑠𝑢𝑚𝑗 𝑛𝑗𝑖 − 𝑛𝑖𝑖
19. Result
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All ground truth boxes with an IoU ratio
less than 0.3 will be labeled as negative
Total Loss function per epoc (in thousand)
20. Comparison
RFCN Resnet 101 68.3% Add Text
GoogleNet Inception V3 55.01% Content Here
SSD Inception V1 73.56% Add Text
Our study 83.45% Content Here
Accuracy Error Rate
Model
22. Conclusion
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Successfully detects dental caries and root canal
Improvement of Faster R-CNN framework for generic
object detection
Performs better than other standalone methods
Good convergence with better local minima
23. Future Works
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Will work on huge dataset
GPU implementation
To build a real time model that can detect dental objects
from videos considering the clinical parameters
24. References
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using Deep CNN algorithm,” 2020 4th International Conference on Trends in Electronics
and In-formatics (ICOEI), 2020.
[2]. J.H. Lee, D.H. Kim, S.N. Jeong and S.H. Choi, ”Diagnosis and prediction of period on
tally compromised teeth using a deep learning-based convolutional neural network
algorithm” ,Journal of periodontal implant science, vol. 48, no. 2, pp. 114-123, 2018.
[3]. A.A. Al Kheraif, A.A. Wahba and H. Fouad, ”Detection of dental diseases from
radiographic2d dental image using hybrid graph-cut technique and convolutional neural
network”, Mea-surement, vol. 146, pp. 333-342, 2019.
[4]. Oralhealth, https://www.who.int/news-room/fact-sheets/detail/oral-health. Last
accessed 19 Aug 2021.
[5]. J.-H. Lee, D.-H. Kim, S.-N. Jeong, and S.-H. Choi, “Detection and diagnosis of dental
caries using a deep learning-based convolutional neural network algorithm,” Journal of
dentistry, vol.77, pp. 106–111, 2018.