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---
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:
- **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 |