Quantification
R–Net is a custom-built Convolutional Neural Network (CNN) developed for automated, segmentation-free quantification of cell types in microscopy images. Created as part of an MSc Biomedical Engineering project at Imperial College London, R–Net achieves high predictive accuracy while significantly reducing the need for manual processing.
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🧬 Segmentation-Free Approach
Directly predicts percentage composition of 3 cell types from raw microscopy images. -
🧠 Custom CNN (R–Net)
Multi-block architecture inspired by human vision hierarchy, using stacked convolutions, batch norm, and dropout. -
📊 Superior Accuracy
R² = 0.7576, MSE = 0.0072, outperforming VGG16, ResNet, MobileNet, and a baseline MNIST-derived model. -
⚙️ End-to-End Workflow
Includes synthetic dataset generation, data augmentation, model training, evaluation, and visualisation.
The full methodology, results, and discussion are available in the final MSc dissertation:
📥 Download PDF
📅 Submitted: September 2023
🏫 Institution: Imperial College London
👨🏫 Supervisors: Prof. Anil Bharath, Dr. Faraz Janan
| Model | MSE | MAE | R² | MAPE | RMSE | Time (hrs) |
|---|---|---|---|---|---|---|
| R–Net | 0.0072 | 0.0684 | 0.7576 | 67.26% | 0.0849 | ~1.5 |
| ResNet | 0.0107 | 0.0899 | 0.4025 | – | – | ~4 |
| VGG16 | 0.0116 | 0.0920 | 0.3215 | – | – | ~4 |
| MobileNet | 0.0278 | 0.1017 | 0.0959 | – | – | ~3 |
| MNIST Baseline | 0.0316 | 0.1051 | 0.3921 | 112.6% | 0.1778 | ~1.5 |
- Python · TensorFlow · Keras · OpenCV · ImgAug · scikit-learn
- Jupyter Notebook · Matplotlib
- Add skip connections (e.g., ResNet-style)
- Apply to real microscopy data for robustness testing
- Tune regularisation (L1/L2), augmentations, and learning rates
If referencing this project or methodology:
Siddiqi, O. (2023). R–Net: A Novel Approach Towards the Cell Quantification of Microscopy Images Using a Regression-based Network. MSc Dissertation, Imperial College London.
- Prof. Anil Bharath – Dataset generation & academic supervision
- Dr. Faraz Janan – Research guidance and technical feedback
© 2023 Owais M Siddiqi — owaissiddiqi.co.uk • LinkedIn • omsiddiqi01@gmail.com