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README.md

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+# 🩺 DeepDerma: Skin Lesion Classification App
+
+Welcome to **DeepDerma**, a simple yet powerful AI tool that helps identify **7 common skin lesions (abnormal injury or disease)** from clinical dermatoscopic images. Upload a skin image, and DeepDerma will predict the most likely diagnosis — assisting in early detection and educational awareness.
+
+
+
+---
+
+## 🔍 How It Works
+
+Just upload a skin lesion image, and our AI model will:
+- Preprocess the image
+- Classify it into one of 7 dermatological categories
+- Return the top predicted class with confidence scores
+
+The model is built using **EfficientNet-B2** and trained on the **DermMNIST** dataset from MedMNIST.
+
+---
+
+## 🧪 Performance Summary
+
+| Metric       | Value     |
+|--------------|-----------|
+| Test Accuracy | 73.3%     |
+| AUC Score     | 0.91   |
+| Top Class F1  | 0.86 (Nevus - NV) |
+| Minority Class F1 | 0.53 |
+
+## Competitiveness
+
+> our results outperforms benchmarks such as ResNet-18, ResNet-50 in terms of accuracy and is competitive in AUC scores
+
+Despite class imbalance, the model performs well on high-priority categories like melanoma (MEL) and nevi (NV) thanks to AUC-based training.
+
+---
+
+## 🧠 Model Details
+
+- **Architecture**: [EfficientNet-B2](https://arxiv.org/abs/1905.11946)
+- **Fine-tuned** on: DermMNIST (medmnist v2)
+- **Input size**: 224 × 224
+- **Optimizer**: Adam, LR = 1e-4
+- **Scheduler**: ReduceLROnPlateau
+- **Augmentations**: Random flip, rotation, color jitter
+- **Class balancing**: Weighted loss + WeightedRandomSampler
+- **Metric used**: AUC (Area Under ROC Curve) for better performance on imbalanced classes
+
 ---
-title: DeepDerma
-emoji: ⚡
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 5.36.2
-app_file: app.py
-pinned: false
-license: mit
-short_description: Detect whether you have skin cancer, tumor or moles
+
+## 📊 Dataset: DermMNIST
+
+- **Source**: [MedMNIST v2](https://medmnist.com/)
+- **Images**: 10,015 dermatoscopic RGB images (28×28, resized to 224×224)
+- **Classes**: 7 types of skin lesions
+- **Split**:
+  - Train: 7,007 images
+  - Val: 1,003 images
+  - Test: 2,005 images
+
+---
+
+## 🧬 Target Classes (With Description)
+
+| Label | Name (Short) | Description |
+|-------|--------------|-------------|
+| 0 | **AKIEC** | Actinic keratoses / Intraepithelial carcinoma – pre-cancerous skin lesions |
+| 1 | **BCC** | Basal Cell Carcinoma – common and locally invasive skin cancer |
+| 2 | **BKL** | Benign Keratosis-like lesions – non-cancerous growths (seborrheic, solar, etc.) |
+| 3 | **DF** | Dermatofibroma – benign skin nodules caused by overgrowth of fibrous tissue |
+| 4 | **MEL** | Melanoma – the most dangerous type of skin cancer; early detection critical |
+| 5 | **NV** | Melanocytic Nevi – common moles, typically benign |
+| 6 | **VASC** | Vascular Lesions – angiomas, hemorrhages, and similar blood vessel-related growths |
+
+---
+
+## 🚀 How to Run
+
+This Space runs using **Gradio**. No setup needed — just:
+
+1. Click the upload button
+2. Select or drag an image
+3. View the predicted class and probabilities
+
+---
+
+## 🧾 Files Included
+
+- `app.py` — Gradio interface
+- `model.py` — Model architecture and prediction pipeline
+- `requirements.txt` — Dependencies
+- `fine_tuned_effnetb2_dermamnist.pth` — Trained model weights
+
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+
+
+