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  • Deep Learning

Deep Learning-Based Gingivitis Detection

A deep learning system for detecting gingivitis (gum disease) from oral/dental images.

Overview

A CNN classifies gingivitis presence from oral cavity images, with automated preprocessing preparing dental photographs for consistent model input and a web interface returning an instant diagnostic result on upload.

Problem Statement

Gingivitis is reversible when caught early and progresses to irreversible periodontal damage when it is not, yet most people only see a dentist once symptoms are advanced.

Proposed Solution

Enable screening from an ordinary oral photograph, so early signs can be flagged without a clinic visit and the patient is prompted to seek treatment while it still reverses.

Key Features

  • CNN-Based Image Classification

    Detects gingivitis presence from oral cavity images.

  • Automated Preprocessing

    Prepares dental images for consistent model input.

  • Web-Based Prediction Interface

    Upload an image and receive an instant diagnostic result.

Technologies Used

Deep Learning

  • CNN
  • TensorFlow / Keras
  • Transfer learning

Image processing

  • OpenCV
  • Normalisation
  • Augmentation

Application

  • Flask
  • Bootstrap
  • SQLite

Project Workflow

  1. 1

    Image upload

    An oral cavity photograph is submitted.

  2. 2

    Preprocessing

    The image is normalised for consistent input.

  3. 3

    Classification

    The CNN predicts gingivitis presence.

  4. 4

    Result

    The diagnostic outcome is returned instantly.

What You'll Receive

  • Complete, runnable source code with folder structure
  • Project report and technical documentation
  • Ready-to-present PPT content
  • Setup and installation walkthrough
  • Line-by-line project explanation session
  • Viva question bank with answers
  • Bug fixing and troubleshooting help
  • Post-delivery support after submission

Frequently Asked Questions

How much does this project cost?

Every project is quoted individually, because the price depends on the modules you need, your technology stack, your college's format and your deadline. Send us the project name on WhatsApp and we'll share a quote the same day.

Can the project be customised to my college requirements?

Yes. Share your guide's requirements, preferred technology stack and abstract format, and we adapt the modules, dataset or UI accordingly.

Will I be able to explain this project during my viva?

That is the point of the explanation session. We walk you through the architecture, every module, the flow of data and the results, and hand over a viva question bank with answers.

What if the project does not run on my laptop?

We help you with setup end to end — dependencies, environment, database and configuration — over chat or a call until it runs on your machine.

Do I get support after submission?

Yes. Post-delivery support is included, so you can come back for fixes, doubts or demo help even after the project is delivered.

Want This Project?

Get complete source code, documentation, PPT, explanation, and support.

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