You can deploy this model and start earning money today!

Koushim/
breast-cancer-swin-classifier

Name: breast-cancer-swin-classifier
Availability: InStock

Views

No views yet

🩺 Breast Cancer Classification Using Swin Transformer (Hugging Face)

This repository contains a fine-tuned Swin Transformer Tiny model from Hugging Face trained on a colorized CT and X-ray breast cancer dataset for binary classification. The model classifies images into:

Malignant (1)
Benign / Normal (0)

The dataset includes colorized medical images enhanced with advanced colorization techniques to aid diagnostic AI models and medical professionals.

📊 Dataset Information

Dataset Name: Medical Imaging (CT-Xray) Colorization New Dataset
Author: Shuvo Kumar Basak (2025)
Hosted on: Kaggle Dataset
DOI: 10.34740/KAGGLE/DSV/11072909

Note: This dataset focuses on CT and X-ray images colorized with various methods to improve visual interpretation and enhance model training. The model does not include the dataset itself.

🧠 Model Architecture

Model Type: Swin Transformer Tiny (pretrained on ImageNet)
Framework: PyTorch + Hugging Face Transformers
Base Model: microsoft/swin-tiny-patch4-window7-224
Model Source: microsoft/swin-tiny-patch4-window7-224
Input Size: 224x224 RGB
Loss Function: CrossEntropyLoss
Optimizer: Adam
Fine-tuning: Final classification head modified for 2 classes

🏁 Training Pipeline Summary

Image Preprocessing:
- Using Hugging Face AutoImageProcessor for normalization and resizing
- Data augmentations via torchvision.transforms (horizontal/vertical flips, rotations, etc.)
Data Loading:
- Dataset split into train, validation, and test sets
- Loaded with torchvision.datasets.ImageFolder and wrapped in DataLoader
Model:
- Loaded pretrained Swin Transformer Tiny from Hugging Face
- Classification head adapted for binary classification (num_labels=2)
Training:
- Optimizer: Adam
- Loss: CrossEntropyLoss
- Training on GPU
Saving:
- Model weights saved as pytorch_model.bin
- Hugging Face processor saved as needed

📈 Results

Metric	Value
Train Accuracy	99.05%
Val Accuracy	82.25%
Test Accuracy	79.00%
Test Loss	0.9054

🔍 Intended Use

This model is intended for:

Educational and research purposes in medical image analysis
Building AI tools for breast cancer detection using colorized CT and X-ray images
Demonstrating transfer learning with Hugging Face Swin Transformer models on medical datasets

⚠️ Not for clinical diagnosis or deployment without further validation and regulatory approval.

🚀 Inference Example (Python)

python

from transformers import AutoImageProcessor, SwinForImageClassification
from PIL import Image
import torch

# Load model and processor
model_path = "Koushim/breast-cancer-swin-classifier"  # Your HF repo
processor = AutoImageProcessor.from_pretrained(model_path)
model = SwinForImageClassification.from_pretrained(model_path).eval()

# Load and preprocess image
image = Image.open("your_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Predict
with torch.no_grad():
    outputs = model(**inputs)
    predicted_class = outputs.logits.argmax(-1).item()

labels = ["benign", "malignant"]
print("Predicted class:", labels[predicted_class])