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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH
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ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
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Paper Title:	Deep Learning Approach for Detection of Dental Caries in X -Ray Images
Authors Name:	Jyothi G C , Dr. Chetana Prakash , Dr. Babitha G. A , Kiran Kumar G. H.
Unique Id:	IJSDR2306193
Published In:	Volume 8 Issue 6, June-2023
Abstract:	Dental caries is the most prevalent disease in the world, affecting more than 3.5 billion people. Dental caries must be treated, which costs money and takes time in every nation's healthcare system. Early disease detection can improve a patient's prognosis and lower the cost of treatment. X-ray imaging is the method used to detect dental caries most frequently after the visual method. A proximal and early-stage carious lesion can be easily missed by the visual examination, so X-ray imaging is very beneficial for early detection and a chance of healing without the need for additional dental care. Using M-FCM and Level Set Techniques, this paper addresses the problems of segmenting dental X-ray images and detecting caries using Faster R- CNN and YOLO V5 Deep Learning algorithms. A dataset of 1200 X-ray images with 800 dental caries annotations was generated. We used it to compare the performance of various architectures that we trained for object detection.
Keywords:	Dental caries detection, M-FCM, Level Set, Faster R-CNN, YOLO V5, Deep Learning, X-ray images.
Cite Article:	"Deep Learning Approach for Detection of Dental Caries in X -Ray Images", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 6, page no.1397 - 1408, June-2023, Available :http://www.ijsdr.org/papers/IJSDR2306193.pdf
Downloads:	000337355
Publication Details:	Published Paper ID: IJSDR2306193 Registration ID:207129 Published In: Volume 8 Issue 6, June-2023 DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.36206 Page No: 1397 - 1408 Publisher: IJSDR \| www.ijsdr.org ISSN Number: 2455-2631

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