This documentation presents the final project output for the Cervical Cancer Classification system. The project uses Deep Learning for feature extraction and a Classical Machine Learning approach for classification.
The goal is to classify cervical cell images into two categories:
- Normal — Healthy cervical cells.
- Abnormal — Cells showing precancerous or cancerous traits.
The system uses a pre-trained ResNet152 CNN for feature extraction and a Logistic Regression model for classification.
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Dataset Preparation
- Consolidated original 5-class dataset into binary form.
- Split data into training (80%) and testing (20%) subsets.
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Feature Extraction
- Used ResNet152 (without the final FC layer) to generate 2048-D feature vectors for each image.
-
Classifier Training
- Trained a Logistic Regression model using the extracted features.
- Applied StandardScaler for normalization.
-
Web Deployment
- Integrated the model into a Flask web app for user-friendly image predictions.
| Metric | Value |
|---|---|
| Accuracy | 98.15% |
| Precision | 98.00% |
| Recall | 98.00% |
| F1-Score | 98.00% |
When a user uploads an image through the web interface:
├─ .gitignore
├─ README.md
├─ requirements.txt
└─ src
├─ classical_models
│ └─ train_classifier.py
├─ data_dat_deletion.py
├─ data_preperation
│ ├─ binary_folders.py
│ ├─ preprocess.py
│ └─ split_dataset.py
├─ feature_extraction
│ └─ feature_extraction.py
├─ predictor
│ ├─ image_predictor.py
│ └─ init.py
├─ test
│ └─ test_image.py
└─ webapp
├─ app.py
└─ templates
└─ predictor.html
This project successfully demonstrates how combining Deep Learning for feature extraction with Classical ML classification yields efficient and interpretable results in medical image analysis. The web-based interface makes the model accessible for non-technical users, contributing toward AI-assisted cervical cancer screening.