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:

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

Eraly-ml
/

Skin-AI