Blood Cell Classification with Deep Learning

Course: Artificial Neural Networks and Deep Learning (AN2DL)
Team Members: Denis Sanduleanu, Jonatan Sciaky, Alessio Caggiano, Mattia Repetti Project Group Name: Gan’s n Roses

📌 Overview

This project focuses on classifying images of blood cells into 8 categories using state-of-the-art Deep Learning techniques. Starting from a custom CNN and evolving through transfer learning and advanced augmentation strategies, we progressively improved our model's generalization and accuracy.

🧠 Problem

• Dataset: 13,759 RGB images (96×96) across 8 blood cell classes.
• Challenges:
  • Class imbalance.
  • Limited dataset size.
  • Generalization gap between local results and external test platform.

🛠️ Approach

We followed an incremental approach:

1. Custom CNN – Initial attempt, later abandoned due to poor generalization.
2. Transfer Learning – Used pre-trained ConvNeXt models (Small, Base, Large).
3. Augmentation – Implemented advanced techniques like:
   • CutMix
   • MixUp
   • RandAugment
   • GridMask
   • CutOut
   • Custom masking
4. Regularization & Optimization:
   • Dropout layers
   • L1/L2 regularization
   • Early stopping
   • Optimizers: Adam, Nadam, AdamW, Lion

📊 Results

Best performance achieved with ConvNeXtLarge, trained with the following setup:

• Batch size: 256
• Training set: 42,000 images (augmented)
• Validation set: Augmented with RandAug (70%)
• Fine-tuning: Last 25–40% of layers trainable
• Optimizer: AdamW (lr=1e-3, weight_decay=1e-5)
• Dropout: 35%
• Regularization: L1L2 (1e-3)
• EarlyStopping: Patience = 4 (fine-tuning phase)

Final accuracy on the platform: 83%

📦 Repository Contents

• PreProcess.ipynb: Notebook for dataset cleaning, outlier removal, and preprocessing (e.g., masking).
• balanced_augmentation.ipynb: Implements and tests various advanced data augmentation strategies including RandAug, CutMix, and MixUp to handle class imbalance and improve generalization.
• custom.ipynb: A custom CNN architecture built from scratch, used as a baseline for comparison.
• convnextbase.ipynb: Model training and evaluation using the ConvNeXt Base architecture with transfer learning.
• ConvNeXtSmall.ipynb: Implementation and tuning of ConvNeXt Small for classification.
• convNextlarge.ipynb: Final and best-performing model using ConvNeXt Large with extensive augmentation, fine-tuning, and regularization.
• Report1.pdf: Full project report with methodology, results, analysis, and conclusions.

📚 References

• CutMix, MixUp, and RandAugment in KerasCV
• Explainable AI for blood cell classification

🔍 Future Work

• Implement model ensembling to further boost accuracy.
• Extend classifier to other types of cell classification tasks.
• Integrate explainability techniques for medical usability.