Exploring the Role of Convolutional Neural Networks (CNN) in Dental
Radiography Segmentation: A Comprehensive Systematic Literature Review
- URL: http://arxiv.org/abs/2401.09190v1
- Date: Wed, 17 Jan 2024 13:00:57 GMT
- Title: Exploring the Role of Convolutional Neural Networks (CNN) in Dental
Radiography Segmentation: A Comprehensive Systematic Literature Review
- Authors: Walid Brahmi and Imen Jdey and Fadoua Drira
- Abstract summary: This work demonstrates how Convolutional Neural Networks (CNNs) can be employed to analyze images, serving as effective tools for detecting dental pathologies.
CNNs utilized for segmenting and categorizing teeth exhibited their highest level of performance overall.
- Score: 1.342834401139078
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In the field of dentistry, there is a growing demand for increased precision
in diagnostic tools, with a specific focus on advanced imaging techniques such
as computed tomography, cone beam computed tomography, magnetic resonance
imaging, ultrasound, and traditional intra-oral periapical X-rays. Deep
learning has emerged as a pivotal tool in this context, enabling the
implementation of automated segmentation techniques crucial for extracting
essential diagnostic data. This integration of cutting-edge technology
addresses the urgent need for effective management of dental conditions, which,
if left undetected, can have a significant impact on human health. The
impressive track record of deep learning across various domains, including
dentistry, underscores its potential to revolutionize early detection and
treatment of oral health issues. Objective: Having demonstrated significant
results in diagnosis and prediction, deep convolutional neural networks (CNNs)
represent an emerging field of multidisciplinary research. The goals of this
study were to provide a concise overview of the state of the art, standardize
the current debate, and establish baselines for future research. Method: In
this study, a systematic literature review is employed as a methodology to
identify and select relevant studies that specifically investigate the deep
learning technique for dental imaging analysis. This study elucidates the
methodological approach, including the systematic collection of data,
statistical analysis, and subsequent dissemination of outcomes. Conclusion:
This work demonstrates how Convolutional Neural Networks (CNNs) can be employed
to analyze images, serving as effective tools for detecting dental pathologies.
Although this research acknowledged some limitations, CNNs utilized for
segmenting and categorizing teeth exhibited their highest level of performance
overall.
Related papers
- Detection and Classification of Diabetic Retinopathy using Deep Learning
Algorithms for Segmentation to Facilitate Referral Recommendation for Test
and Treatment Prediction [0.0]
This research paper addresses the critical challenge of diabetic retinopathy (DR), a severe complication of diabetes leading to potential blindness.
The proposed methodology leverages transfer learning with convolutional neural networks (CNNs) for automatic DR detection using a single fundus photograph.
High evaluation scores in Jaccard, F1, recall, precision, and accuracy underscore the model's potential for enhancing diagnostic capabilities in retinal pathology assessment.
arXiv Detail & Related papers (2024-01-05T11:19:24Z) - Radiology Report Generation Using Transformers Conditioned with
Non-imaging Data [55.17268696112258]
This paper proposes a novel multi-modal transformer network that integrates chest x-ray (CXR) images and associated patient demographic information.
The proposed network uses a convolutional neural network to extract visual features from CXRs and a transformer-based encoder-decoder network that combines the visual features with semantic text embeddings of patient demographic information.
arXiv Detail & Related papers (2023-11-18T14:52:26Z) - Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and
Methodologies from CNN, GAN to Attention and Transformers [72.047680167969]
This article aims to introduce the deep learning based data driven techniques for fast MRI including convolutional neural network and generative adversarial network based methods.
We will detail the research in coupling physics and data driven models for MRI acceleration.
Finally, we will demonstrate through a few clinical applications, explain the importance of data harmonisation and explainable models for such fast MRI techniques in multicentre and multi-scanner studies.
arXiv Detail & Related papers (2022-04-01T22:48:08Z) - Deep Learning for Computational Cytology: A Survey [12.08083533402352]
We introduce various deep learning methods, including fully supervised, weakly supervised, unsupervised, and transfer learning.
Then, we systematically summarize the public datasets, evaluation metrics, versatile cyto image analysis applications including classification, detection, segmentation, and other related tasks.
arXiv Detail & Related papers (2022-02-10T16:22:10Z) - Modality specific U-Net variants for biomedical image segmentation: A
survey [0.6091702876917281]
This article contributes to describe the U-Net framework, followed by the comprehensive analysis of the U-Net variants for different medical imaging or modalities.
Also highlights the contribution of U-Net based frameworks in the on-going pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19.
arXiv Detail & Related papers (2021-07-09T16:41:40Z) - Catalyzing Clinical Diagnostic Pipelines Through Volumetric Medical
Image Segmentation Using Deep Neural Networks: Past, Present, & Future [0.0]
This paper will briefly overview some of the state-of-the-art (SoTA) neural network-based segmentation algorithms.
It will also demonstrate important clinical implications of effective deep learning-based solutions.
arXiv Detail & Related papers (2021-03-27T19:05:11Z) - A Feasibility study for Deep learning based automated brain tumor
segmentation using Magnetic Resonance Images [0.0]
A deep convolutional neural network (CNN) based classification network and Faster RCNN based localization network were developed for brain tumor MR image classification and tumor localization.
Overall performance of the proposed tumor segmentation architecture, was analyzed using objective quality parameters including Accuracy, Boundary Displacement Error (BDE), Dice score and confidence interval.
It was observed that the confidence level of our segmented output was in a similar range to that of experts.
arXiv Detail & Related papers (2020-12-22T12:11:42Z) - Explaining Clinical Decision Support Systems in Medical Imaging using
Cycle-Consistent Activation Maximization [112.2628296775395]
Clinical decision support using deep neural networks has become a topic of steadily growing interest.
clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend.
We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
arXiv Detail & Related papers (2020-10-09T14:39:27Z) - Learning Binary Semantic Embedding for Histology Image Classification
and Retrieval [56.34863511025423]
We propose a novel method for Learning Binary Semantic Embedding (LBSE)
Based on the efficient and effective embedding, classification and retrieval are performed to provide interpretable computer-assisted diagnosis for histology images.
Experiments conducted on three benchmark datasets validate the superiority of LBSE under various scenarios.
arXiv Detail & Related papers (2020-10-07T08:36:44Z) - Spatio-spectral deep learning methods for in-vivo hyperspectral
laryngeal cancer detection [49.32653090178743]
Early detection of head and neck tumors is crucial for patient survival.
Hyperspectral imaging (HSI) can be used for non-invasive detection of head and neck tumors.
We present multiple deep learning techniques for in-vivo laryngeal cancer detection based on HSI.
arXiv Detail & Related papers (2020-04-21T17:07:18Z) - Opportunities and Challenges of Deep Learning Methods for
Electrocardiogram Data: A Systematic Review [62.490310870300746]
The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare.
Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals.
This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives.
arXiv Detail & Related papers (2019-12-28T02:44:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.