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.

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

2. Overview
Problem Statement
Introduction
Objectives
Proposed Methodology
Result
Conclusion
Future Work

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

8. Proposed Methodology
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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

21. Comparison

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.
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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.

25. THANK YOU