ResNet-18 Peripheral Blood Cell Classifier

Model Description

This is a ResNet-18 model fine-tuned for peripheral blood cell (PBC) classification using fastai. The model can classify blood cell images into 8 different cell types with 98.07% validation accuracy.

Model Details

Model Type: ResNet-18 with transfer learning
Framework: fastai (version <2.8.0)
Task: Image Classification
Dataset: Peripheral Blood Cell (PBC) dataset
Classes: 8 cell types
Validation Accuracy: 98.07%

Cell Types

The model can classify the following blood cell types:

Basophil - A type of white blood cell involved in inflammatory reactions
Eosinophil - White blood cells that fight parasites and allergic reactions
Erythroblast - Immature red blood cells
IG (Immature Granulocyte) - Immature white blood cells
Lymphocyte - White blood cells that fight infections
Monocyte - Large white blood cells that become macrophages
Neutrophil - Most common white blood cells that fight bacterial infections
Platelet - Cell fragments that help blood clotting

Training Details

Training Images: 13,674
Validation Images: 3,418
Architecture: Pretrained ResNet-18 backbone with custom head
Training Strategy:
- 4 epochs with frozen backbone
- 6 epochs with fine-tuning
Input Size: 224x224 pixels
Preprocessing: Standard ImageNet normalization

Performance

Validation Accuracy: 98.07%
All cell types: >95% precision and recall
Best performers: Eosinophil and Platelet (100% precision)

Usage

from fastai.vision.all import *

# Load the model
learn = load_learner('cell_classifier.pkl')

# Predict on an image
pred, pred_idx, probs = learn.predict('path/to/blood_cell_image.jpg')
print(f"Predicted: {pred}")
print(f"Confidence: {probs[pred_idx]:.2%}")

Requirements

fastai>=2.7.0,<2.8.0
numpy<2.0
pillow>=10.0.0

Model Files

cell_classifier.pkl - Complete fastai learner with model and preprocessing
cell_classifier_weights.pth - PyTorch weights only
confusion_matrix.png - Validation confusion matrix
classification_report.csv - Detailed classification metrics
training_summary.json - Training configuration and results

Citation

If you use this model, please cite:

@misc{pbc-cell-classifier-2024,
  title={ResNet-18 Peripheral Blood Cell Classifier},
  author={Your Name},
  year={2024},
  howpublished={Hugging Face Hub},
  url={https://huggingface.co/your-username/pbc-cell-classifier}
}

License

This model is released under the MIT License.

Created For

HuggingFace Agents-MCP-Hackathon Track 1 - MCP Tool/Server