Mask RCNN with RESNET50 for Dental Filling Detection

Aparna, S.; Muppavaram, Kireet; Ramayanam, Chaitanya C V; Ramani, Kasarapu

doi:10.14569/ijacsa.2021.0121079

Cited by 4 publications

(10 citation statements)

References 23 publications

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“…Authors in [7] utilized a dataset of several dental radiographs and employed the Mask RCNN model with the ResNET 50 architecture to generate masks on new dental radiographs. They proved that this model is beneficial in helping a dentist see the exact level of filling that has been performed.…”

Section: Literature Surveymentioning

confidence: 99%

“…These three categories are selected among over 12 specific objects prevailing in dental fillings and implants. These categories have been generalized based on the opinion of an experienced dentist and by referring to the research done in [7]. Besides dentists, this research work may assist common people, who are not familiar with or do not have the expertise to understand a dental X-ray, to comprehend the treatment done, the work precision, and several other details that can be derived from dental radiographs.…”

Section: Introductionmentioning

confidence: 99%

“…A diverse dataset offers ample information for the neural network to learn from, leading to consistent performance even on real-world data. In this research, the dataset provided by [7], which comprises images collected from various dental clinics in Hyderabad, Telangana, India, was utilized. These images, being originally in jpg format with four channels, were converted to three-channel images to reduce computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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Advancements in Dental Filling Detection Technologies and Strategies for Comprehensive Oral Health Care

Aparna,

Gottumukkala,

Shivampet

et al. 2024

Eng. Technol. Appl. Sci. Res.

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Many individuals face issues with their teeth, requiring the expertise of dentists to provide necessary care. Despite the advancements in dental techniques, there is a persistent shortage of dentists, prompting the development of tools to help the latter efficiently perform patient treatment. The current research focuses on refining the precision of the vital dental treatment known as dental fillings. The approach involves utilizing the Mask Region-based Convolutional Neural Network (MaskRCNN) with different variants of ResNET, such as ResNET50, ResNET101 C4, Dilated C5, and Feature Pyramid Network (FPN), to analyze diverse dental radiographs. By training on a broad range of tooth images, this methodology creates a pixel-based masking system, improving dentists' ability to precisely identify filling levels. Consequently, this innovation contributes significantly to expediting and refining the accuracy of dental treatments, ultimately benefiting individuals with tooth problems. Additionally, as a future prospect, this model can enable robots to perform dental operations as it provides pixel-level information necessary for the treatment.

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Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Advancements in Dental Filling Detection Technologies and Strategies for Comprehensive Oral Health Care

Aparna,

Gottumukkala,

Shivampet

et al. 2024

Eng. Technol. Appl. Sci. Res.

Self Cite

View full text Add to dashboard Cite

show abstract

“…1) ResNet50 backbone: This module consists of the pretrained ResNet50 network that serves as the feature extractor. It takes an image as input and outputs a feature map [28]. In Faster RCNN, the ResNet-50 backbone is used as the feature extractor network to produce a compact feature representation of the input image [29]; the input image being fed into the backbone convolutional neural network.…”

Section: B Faster Rcnn With Resnet50mentioning

confidence: 99%

Marigold Flower Blooming Stage Detection in Complex Scene Environment using Faster RCNN with Data Augmentation

Patel¹

2023

IJACSA

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In recent years, flower growing has developed into a lucrative agricultural sector that provides employment and business opportunities for small and marginal growers in both urban and rural locations in India. One of the most often cultivated flowers for landscaping design is the Marigold flower. It is also widely used to create garlands for ceremonial and social occasions using loose flowers. Understanding the appropriate stage of harvesting for each plant species is essential to ensuring the quality of the flowers after they have been picked. It has been demonstrated that human assessors consistently used a category scoring system to evaluate various flowering stages. Deep learning and convolutional neural networks have the potential to revolutionize agriculture by enabling efficient analysis of largescale data. In order to address the problem of Marigold flower stages detection and classification in complex real-time field scenarios, this study proposes a fine-tuned Faster RCNN with ResNet50 network coupled with data augmentation. Faster RCNN is a popular deep learning framework for object detection that uses a region proposal network to efficiently identify object locations and features in an image. The Marigold flower dataset was collected from three different Marigold fields in the Anand District of Gujarat State, India. The collection includes of photos that were taken outdoors in natural light at various heights, angles, and distances. We have developed and fine-tuned a Faster RCNN detection and classification model to be particularly sensitive to Marigold flowers, and we have compared the generated method's performance to that of other cutting-edge models to determine its accuracy and effectiveness.

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“…For improving dental disease diagnosis and treatment plans, deep learning [7] has significantly influenced the processing of intraoral X-ray images in image processing [8], segmentation [9][10][11][12] and enhancements [13][14][15]. These developments integrating intraoral X-ray imaging with deep learning techniques enhance the precision of oral health condition recognition and detection, such as dental caries [16][17][18][19][20][21][22], implant [23], oriented tooth [24], restoration by filling [25] and dental material [26]. This study adapts deep learning techniques on a novel dataset to recognize and detect twenty categories as abscessed teeth, calculus, caries, cysts, dental bridges, dental crowns, extracted teeth, filling overhang, impacted wisdom teeth, implants, mesialized dentition, mixed dentition, periodontal bone loss, pulpitis, restoration by filling, retained root, screw-retained restoration, single-root canal treatment, two-root canal treatment and three-root canal treatment.…”

Section: Introductionmentioning

confidence: 99%

CVApool: using null-space of CNN weights for the tooth disease classification

Can,

Isik,

Anagun

2024

Neural Comput & Applic

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In light of current developments in dental care, dental professionals have increasingly used deep learning methods to get precise diagnoses of oral problems. Using intraoral X-rays in dental radiography is imperative in many dental interventions. Integrating deep learning techniques with a unique collection of intraoral X-ray images has been undertaken to enhance the accuracy of dental disease detection. In this study, we propose an alternative pooling layer, namely the Common Vector Approach Pooling technique, to address the constraints associated with average pooling in deep learning methods. The experiments are conducted on a large dataset, involving twenty different dental conditions, divided into seven categories. Our proposed approach achieved a high accuracy rate of 86.4% in identifying dental problems across the seven oral categories.

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Mask RCNN with RESNET50 for Dental Filling Detection

Cited by 4 publications

References 23 publications

Advancements in Dental Filling Detection Technologies and Strategies for Comprehensive Oral Health Care

Advancements in Dental Filling Detection Technologies and Strategies for Comprehensive Oral Health Care

Marigold Flower Blooming Stage Detection in Complex Scene Environment using Faster RCNN with Data Augmentation

CVApool: using null-space of CNN weights for the tooth disease classification

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