🌸 CerviScan – Website

CerviScan is a web-based application designed to support early cervical pre-cancer detection using Visual Inspection with Acetic Acid (VIA) images. This system integrates a machine learning model developed by our team to classify VIA images into normal or abnormal categories.

The model achieves the following performance:

• Accuracy: 90.91%
• Precision: 93.75%
• Specificity: 93.75%
• Recall: 88.24%
• F1-score: 90.91%

Full machine learning development: 👉 https://github.com/FillipusAditya/cerviscan-cervical-cancer-detection

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🧠 Machine Learning Model Overview

• Model: XGBoost

• Segmentation Technique: Multi-Otsu Thresholding

• Feature Extraction:

  • Color Moment (YUV)
  • Texture: GLRLM, Tamura, LBP

• Dataset: IARC Colposcopy Image Bank

  • Final: 162 training images (75 abnormal, 87 normal)

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🛠️ Technology Stack

• HTML, CSS
• Python Flask

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✨ Website Features

• User Registration

• Login & Logout

• Patient Profile Input (first name, last name, date of birth)

• Upload VIA images for detection

• Visualization of detection output:

  • Uploaded image
  • Grayscale image
  • Segmentation mask
  • Segmented result
  • Final detection result

• Detection history page (view & delete past results)

• Usage guide page

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👩‍🏫 Supervisors

1. Prof. Dr. Eng. Ir. Retno Supriyanti, S.T., M.T.
2. Mohammad Irham Akbar, S.Kom., M.Cs.
3. Yogi Ramadhani, S.T., M.Eng.
4. Katon Muhammad, S.T., M.T.

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🏥 Medical Partners

1. dr. Futiat Diana Kartika
2. Kartika Dwi Hapsari, S.Tr.Keb

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👨‍💻 Development Team

Electrical Engineering, Universitas Jenderal Soedirman

1. Fillipus Aditya Nugroho
2. M. Saujana Shafi Kehaulani
3. Tegar Dwi Agung Saputra
4. M. Rizqy Maulana Sarwono

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🎓 Supported by

Kementerian Pendidikan Tinggi, Sains dan Teknologi Scheme: "Penelitian Terapan" 2025–2026

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📌 Important Note

This repository is part of an undergraduate thesis project and is intended for academic and research purposes only. The dataset from IARC Colposcopy Image Bank is not redistributed within this repository and must be obtained directly from the original source according to its usage license.

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🚀 How to Run the Website Locally

1. Clone the Repository

git clone https://github.com/FillipusAditya/cerviscan-website.git

2. Enter the Project Directory

cd cerviscan-website

3. Install Dependencies

pip install -r requirements.txt

4. Run the Flask App

python app.py

5. Open in Browser

The application will run at:

http://127.0.0.1:5000/

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🙏 Thank You!

Thank you for your interest in CerviScan – Website! 🧬✨

For any questions or collaboration opportunities, feel free to reach out.