🔬 Cancer Classification using Convolutional Neural Networks

A deep learning project comparing three CNN architectures for multi-class cancer classification from histopathological images.

📋 Overview

This project implements and compares three different Convolutional Neural Network approaches for classifying cancer types from medical images:

Model	Description
Baseline CNN	Custom-built CNN architecture from scratch
Improved CNN	Enhanced architecture with optimized hyperparameters
Transfer Learning	Pre-trained model fine-tuned for cancer classification

📊 Dataset

• Size: 25,000 histopathological images
• Classes: 5 different cancer types
• Source: Academic Torrents

⚠️ The dataset is too large to host on GitHub. Please download it from the link above.

🏗️ Project Structure

├── Assignment_DL_[Ivaylo Papazov].ipynb   # Main notebook with all implementations
├── Deep Learning Assignment Template.pdf # Detailed report and analysis
└── README.md

🚀 Getting Started

Prerequisites

pip install tensorflow numpy pandas matplotlib scikit-learn

Usage

1. Download the dataset from the link above
2. Open the Jupyter notebook
3. Update the data path to your local dataset location
4. Run all cells to train and evaluate the models

📈 Results

The notebook includes:

• Training/validation accuracy curves
• Confusion matrices
• Comparative analysis across all three architectures
• Hyperparameter tuning experiments

For detailed results and analysis, refer to the PDF report.

📝 Key Findings

• Comparison of model performance across different architectures
• Impact of hyperparameter tuning on classification accuracy
• Effectiveness of transfer learning for medical image classification

📚 References

• Dataset: LC25000 Lung and Colon Histopathological Image Dataset

👤 Author

Ivaylo Papazov

📄 License

This project is available for educational purposes.