Aditya-Sai-19/diabetic-retinopathy-swin

Diabetic Retinopathy Prediction — MobileNetV2

This model predicts the severity grade of diabetic retinopathy (DR) from retinal fundus images. It classifies images into 5 grades:

Model Details

• —Architecture: MobileNetV2 (2.2M parameters) fine-tuned for 5-class DR grading
• —Input: 224×224 RGB retinal fundus images
• —Training Data: youssefedweqd/Diabetic_Retinopathy_Detection — 25,290 train / 2,810 val / 7,026 test images
• —Loss Function: Focal Loss (γ=2.0) with inverse-frequency class weights — critical for handling severe class imbalance
• —Primary Metric: Quadratic Weighted Kappa (QWK) — the standard metric for ordinal DR grading

Training Recipe

Based on published DR detection literature (see References below):

Class Distribution (severe imbalance)

Results

Validation Performance (best epoch = 3)

Test Set Performance

• —Accuracy: 13.5%
• —QWK: 0.384

Per-Class Test Results

Usage

from transformers import pipeline

classifier = pipeline("image-classification", model="Aditya-Sai-19/diabetic-retinopathy-swin")
result = classifier("path/to/retinal_image.jpg")
print(result)
# [{'label': 'Moderate', 'score': 0.45}, {'label': 'Mild', 'score': 0.22}, ...]

Or manually:

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

processor = AutoImageProcessor.from_pretrained("Aditya-Sai-19/diabetic-retinopathy-swin")
model = AutoModelForImageClassification.from_pretrained("Aditya-Sai-19/diabetic-retinopathy-swin")

image = Image.open("retinal_scan.jpg")
inputs = processor(image, return_tensors="pt")

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

print(f"Predicted: {model.config.id2label[predicted_class]}")

Limitations & Recommendations for Improvement

This model was trained on CPU only (free tier) with a lightweight MobileNetV2 backbone. For production-quality DR grading, consider:

1. 1.Use a larger backbone: Swin Transformer Base or EfficientNet-B4 (published SOTA: QWK > 0.93 on APTOS)
2. 2.Train on GPU: Enables larger batch sizes, more epochs, and larger models
3. 3.Ben Graham preprocessing: Apply contrast enhancement (I_ce = 4*I - 4*G(σ=w/90)*I + 128) and FOV cropping — this alone adds +2-5% AUC
4. 4.More data: Merge APTOS + Messidor-2 + IDRiD + EyePACS for better generalization
5. 5.Data augmentation: 10x oversampling of minority classes (grades 3 & 4)
6. 6.Post-processing: Optimized ordinal thresholds for QWK maximization

References

• —Dual Branch Deep Learning Network for Detection and Stage Grading of DR (arXiv:2308.09945) — QWK=0.930
• —A ResNet is All You Need? (arXiv:2210.03180) — AUC=0.955
• —Ord2Seq: Ordinal Regression as Label Sequence Prediction (arXiv:2307.09004) — Acc=84.2%

Framework Versions

• —Transformers 5.6.2
• —PyTorch 2.11.0
• —Datasets 4.8.4