---
license: mit
language:
- en
metrics:
- accuracy
base_model:
- facebook/convnextv2-tiny-1k-224
pipeline_tag: image-classification
tags:
- medical
- transformers
---
# MedConvNeXt: Optimized Skin Disease Classification

## 📌 Introduction
MedConvNeXt is a deep learning model based on ConvNeXt, optimized for skin disease classification using PyTorch Lightning. The model leverages hyperparameter tuning via Optuna to enhance its performance over multiple training iterations.

## 📂 Dataset
The dataset consists of images of various skin diseases, structured as follows:
```
SkinDisease/
    train/
        class_1/
        class_2/
        ...
    test/
        class_1/
        class_2/
        ...
```
Data augmentation techniques such as **AutoAugment, horizontal flipping, rotation, color jittering, and random erasing** were applied to improve model generalization.

## ⚙️ Model Architecture
- **Base Model:** ConvNeXt-Base (pretrained on ImageNet)
- **Optimizer:** AdamW with CosineAnnealingLR scheduler
- **Loss Function:** CrossEntropyLoss / Focal Loss (for class imbalance handling)
- **Evaluation Metrics:** Accuracy, Precision, Recall, and F1-score
- **Hyperparameter Optimization:** Optuna (10 trials, 5 epochs per trial)

## 📊 Training Process
The model was trained using PyTorch Lightning with automatic logging to TensorBoard for real-time monitoring. The best hyperparameters were selected using Optuna, and the final model was trained over 23 epochs.

## 🚀 Results
Below are key performance graphs from TensorBoard:

![Training Metrics](plot.jpg)

- **Accuracy & Precision improved with hyperparameter tuning**
- **Training loss consistently decreased, showing model convergence**

## 🔗 How to Use
To load and use the model:
```python
import torch
from torchvision import transforms
from PIL import Image

# Load the model
model = torch.jit.load("skinconvnext_scripted.pt")
model.eval()

# Define image transformation
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

# Predict a sample image
image = Image.open("sample.jpg").convert("RGB")
image_tensor = transform(image).unsqueeze(0)
output = model(image_tensor)
predicted_class = torch.argmax(output, dim=1).item()
print("Predicted Class:", predicted_class)
```

## 📌 Future Work
- **Clinical validation** on real-world medical datasets
- **Model interpretability** via Grad-CAM or SHAP
- **Deployment optimization** using ONNX and TensorRT

## 📝 License
This project is intended for research and educational purposes only. For clinical use, further validation is required.

---
**Hugging Face Space:** [https://huggingface.co/spaces/Eraly-ml/Skin-AI]

**Author:** [Eraly Gainulla]

My telegram @eralyf