Automated Dental Cavity Detection Using Machine Learning

Abstract

The prevalence of dental cavities and the importance of early detection and treatment have long been recognized in the field of dentistry. To address this issue, we present a project review of an automated dental cavity detection system that leverages the power of machine learning techniques. The objective of this study was to develop an accurate system capable of identifying dental cavities in radiographic images with high precision. In this project, we studied diverse dataset of dental X-ray images and implemented a deep learning model to perform cavity detection. It was taught the subtle patterns and characteristics suggestive of dental cavities using a sizable collection of annotated dental photos. Our project focused on three key components: data pre-processing, model training, and evaluation. The results of our study demonstrate the effectiveness of the automated dental cavity detection system. This project evaluation explores the real-world applications of our approach and how it could transform dental healthcare by facilitating early diagnosis and prompt treatment. Furthermore, we are integrating this model with android application for its usefulness to both patients and doctors. The system\'s ability to aid dentists and radiologists in their clinical decision-making process can significantly reduce the burden of dental diseases and improve patient outcomes. In addition to this, the system suggests further steps to patients for their treatment by helping in appointments and slot bookings. In conclusion, our automated dental cavity detection system, developed through the integration of machine learning techniques, exhibits great promise in enhancing the field of dentistry. The project highlights the accomplishments and future prospects of this technology, and its potential to improve preventive dental care and contribute to overall oral health.

Introduction

I. INTRODUCTION

Early cavity diagnosis and treatment are essential to maintaining good oral health, as dental health is a crucial component of general well-being. In recent years, the capabilities of machine learning has brought about revolutionary change in the field of medical diagnostics. Among these advancements, the development of automated dental cavity detection systems stands as a remarkable milestone in the domain of dentistry.

This project aims to shed light on an innovative approach to dental care, where we explore the creation and evaluation of an "Automated Dental Cavity Detection System Using Machine Learning." Dental cavities, also known as dental caries or simply "cavities", are a common dental ailment affecting people of all ages.

The conventional method of diagnosing these cavities often relies on the keen observations of skilled dentists and radiologists, coupled with extensive training and experience. However, the advent of machine learning has opened up new possibilities for more precise, efficient, and consistent cavity detection.

The significance of early detection of dental cavities not only helps prevent further decay and complications but also reduces the overall cost and pain associated with treatment. Through this project, we delve into the development process and results of an automated system that uses machine learning techniques to detect dental cavities from radiographic images.

In this we will explore the methodology employed, the challenges faced, and the achievements realized throughout the course of this project. We will also discuss the implications of such an automated system in the field of dentistry and how it can potentially reshape the way cavities are diagnosed and managed. Furthermore, we will delve into the ethical and practical aspects of implementing this technology in real-world clinical settings through android application, as well as its potential to enhance preventive dental care and improve patient outcomes.

This project is not merely a technological endeavour but a step toward a future where technology empowers healthcare professionals, reduces human error, and enhances the lives of patients. The development of an automated dental cavity detection system represents a promising intersection of technology and medicine, and this review serves as a comprehensive exploration of its capabilities and potential impact.

II. ARCHITECTURE DIAGRAM

III. TECH STACK

The technology stack for a software application designed to empower individuals with disabilities and enhance their vocational training and employment opportunities should be carefully selected to ensure accessibility, scalability, security, and usability. Below are suggested technology stack for this project:

A. Front-end Development

React Native: React Native is an open-source framework for React and JavaScript mobile application development. It offers a native-like user experience by enabling developers to create code once and distribute it on both the iOS and Android platforms.

B. Back-end Development

1. MongoDB: MongoDB is a NoSQL database that stores information in BSON, a versatile format that resembles JSON. It works especially well when managing big volumes of semi-structured or unstructured data.
2. Node.js: A JavaScript runtime called Node.js enables programmers to run server-side programmes. It is renowned for having an event-driven, non-blocking input/output mechanism, which makes it appropriate for creating high-performance, scalable applications.
3. Express.js: A web application framework for Node.js is called Express.js. By offering a collection of capabilities for both online and mobile applications, it makes the process of creating dependable and scalable web applications easier.
4. Python: Python is a flexible language for programming that's often utilised in machine learning applications. To create and train machine learning models, you can utilise well-known machine learning libraries like PyTorch, TensorFlow, and scikit-learn.must be in 24 pt Regular font.  Author name must be in 11 pt Regular font.  Author affiliation must be in 10 pt Italic.  Email address must be in 9 pt Courier Regular font.

In the architecture, the React Native frontend seamlessly interacts with the backend, which manages incoming requests through the organized routes implemented using Express.js. To introduce machine learning capabilities, a dedicated route or endpoint is established within Express.js.

This endpoint acts as a trigger, initiating the execution of a Python script or function. Within the Python script, pre-trained machine learning models are employed, harnessing the valuable data stored in MongoDB for predictive analyses or other machine learning tasks.

Following the execution, the Python script communicates the results back to the corresponding Express.js route. Subsequently, the backend relays this information to the React Native frontend, completing a streamlined and efficient integration of machine learning functionality into the overall application architecture.

IV. IMPLEMENTATION

A. Steps

1. Data Collection: Data collection involves gathering the raw data that will be used to train and test a machine learning model. The data can come from various sources, such as databases, sensors, or external datasets. Collecting the right data is crucial to ensure that the model can learn effectively.
2. Data Pre-processing: Data pre-processing is the process of sanitising and getting obtained data ready for analysis. This include handling missing values, removing duplicates, and handling outliers. Data can also be changed or standardised in order to have it ready for modelling.
3. Feature Extraction: The process of choosing or generating pertinent features (attributes) from the data that are most instructive for the machine learning model is known as feature extraction. In this step, the dimensionality of the data is decreased and the main factors affecting the target variable are highlighted.
4. Model Selection: Model selection is the process of choosing the most appropriate machine learning algorithm or model for the task at hand. This depends on the characteristics of the dataset and the type of task (classification, regression, clustering, etc.).
5. Model Training: A subset of the pre-processed data is used during model training to instruct the chosen machine learning model. With the help of the goal variables and supplied features, the model gains the ability to forecast. To increase the accuracy of the model and fine-tune its parameters, this stage is crucial.
6. Model Evaluation: To make sure the model is effective, its performance must be assessed after training. This is evaluating how effectively the model generalises to new, unobserved occurrences using an alternative set of data (testing data). Among the common evaluation criteria are F1 score, recall, accuracy, and precision.
7. App Integration: App integration refers to the incorporation of the trained machine learning model into an application or system where it can be used to make predictions or provide insights. This step ensures that the model's capabilities are accessible to end-users or other applications.
8. Deployment and Testing: The product is either launched into the market or deployed in the client environment after completing both functional and non-functional testing.
9. Maintenance: There are occasionally issues with the client environment. Patches are released to fix specific issues. In an attempt to make the product better, better versions are also released. Maintenance is done to put these changes into effect in the client environment. Every one of these phases’ transitions into the next, and progress seems to flow over them at a continuous incline, much to a waterfall. The next phase cannot start unless the planned set of goals from the previous one have been completed and accepted. This model does not have cross-over phases.

B. Algorithm

Canny edge detection is a widely used image processing technique that identifies and highlights edges or boundaries in digital images.

It does this by following several steps: smoothing the image to reduce noise, calculating the image gradient to find areas of rapid intensity change, thinning edges to a one-pixel width, and applying high and low thresholds to detect edges accurately. Canny edge detection is particularly useful in computer vision and image analysis for tasks such as object detection and recognition.

Identifying canny edges requires a few crucial steps:

1. Smoothing: Apply a Gaussian filter to reduce noise.
2. Calculate Gradients: Apply Sobel operations to get gradients of intensity.
3. Non-Maximum Suppression: Maintain only local gradient maxima to thin the margins.
4. Double Thresholding: To categorize edges, use high and low thresholds.
5. Tracking the Edge Using Hysteresis: To create continuous shapes, connect weak edges to strong ones. As a consequence, edges can be detected accurately.

In cavity detection, segmentation refers to the process of isolating and identifying areas within dental X-ray images that may indicate dental cavities or anomalies. It separates potential cavity regions from the healthy dental structure, enabling focused analysis and accurate diagnosis. This is crucial for automating the detection of cavities and assessing their severity in dental healthcare.

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Conclusion

We have examined the amazing possibilities of a machine learning-based automated dental cavity detection system in this project evaluation. This innovative system represents a significant leap forward in the field of dental healthcare, offering a powerful solution to improve the accuracy, efficiency, and accessibility of cavity diagnosis. By automating the detection process, the initiative hopes to transform dental treatment through the integration of cutting-edge machine learning algorithms with an Android application. By automatically identifying cavities in dental X-ray images, it empowers both dental professionals and individuals to make informed decisions about their oral health. The project\'s strength lies not only in its ability to deliver precise and real-time cavity detection but also in its commitment to data security, compliance with healthcare standards, and user-friendly interface. By providing educational resources and promoting preventive dental care, the system goes beyond detection, emphasizing the importance of proactive oral health practices. In summary, the Automated Dental Cavity Detection System promises to reduce disparities in healthcare access, foster early cavity detection, and ultimately improve oral health outcomes. With its ambitious goals and innovative approach, this project paves the way for a future where dental care is more accurate, accessible, and proactive.

References

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Copyright

Copyright © 2024 Prof. Pooja More, Ms. Rajeshwari Patil, Ms. Swarali Suryawanshi, Ms. Sakshi Andhare, Ms. Srushti Raut. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.