Blood Cell Classification - Deep Learning Project

CNN, ResNet & Vision Transformer experiments on BloodMNIST dataset.

🩸 Dataset

• BloodMNIST (8 blood cell types)
• 28×28 RGB images
• 11,959 train / 1,712 validation / 3,421 test images
• Classes: Basophil, Eosinophil, Erythroblast, IG, Lymphocyte, Monocyte, Neutrophil, Platelet

🏗️ Models Implemented

1. Custom CNN Architectures

• Base CNN: Conv → ReLU → MaxPool blocks
• With BatchNorm / LayerNorm
• With Dropout (0.05, 0.2, 0.5, 0.7)
• With Weight Decay (1e-2 to 1e-5)
• Optimizers: Adam, SGD, RMSprop, Nesterov Adam
• Learning rate scheduling

2. Transfer Learning

• ResNet18 (torchvision)
• Feature extraction (frozen backbone)
• Fine-tuning (last 2 blocks unfrozen)

3. Vision Transformer

• Pre-trained ViT (DeiT-tiny via timm)
• Input: 224×224 (resized from 28×28)
• Feature extraction & fine-tuning

4. Ensemble

• Soft Voting of 7 best models
• Highest accuracy achieved

📊 Results Summary

Model	Best Accuracy	Notes
CNN (BatchNorm + Adam)	94.68%	Good balance
CNN with Dropout (p=0.05)	94.33%	Reduced overfitting
ResNet18 (fine-tuned)	87.40%	Underperformed custom CNN
Vision Transformer (fine-tuned)	90.18%	Better than ResNet
Ensemble (Soft Voting)	96.55%	Best result

🔧 Tech Stack

• Python 3.8+
• PyTorch, torchvision
• timm (for ViT)
• numpy, matplotlib, scikit-learn