Final Test Accuracy: 95.60%
Evaluated on a balanced, augmented custom dataset of real-world dermatological images.
Confusion Matrix indicates strong separation for all classes, with minor overlap in visually similar conditions.

🏗️ Model Architecture

✅ Backbone: EfficientNetV2B0 (pretrained on ImageNet)
🔁 Global Average Pooling + Dense(512, ReLU) + Dropout(0.4) + Dense(6, Softmax)
📉 Loss Function: sparse_categorical_crossentropy
⚖️ Class Weights: Applied to handle minor variations
🧠 Fine-tuned with learning rate scheduling and layer unfreezing

🧠 Usage

🔍 Load the model (Keras format)

python

from tensorflow.keras.models import load_model
model = load_model("path/to/saved_skin_model")

🖼️ Input Format

Image size: 224x224
Input dtype: float32
Preprocessing: Use preprocess_input from tensorflow.keras.applications.efficientnet_v2

python

from tensorflow.keras.applications.efficientnet_v2 import preprocess_input
import numpy as np
img = preprocess_input(img)  # Ensure shape is (1, 224, 224, 3)
pred = model.predict(img)

📊 Output Format

Softmax probabilities for 6 classes.
argmax(pred) gives class index from 0–5.

🧾 License

MIT License

👨‍💻 Author

Tanishq Shinde B.E. Computer Engineering, PICT 🔗 GitHub | LinkedIn | Hugging Face

🔬 For educational and non-diagnostic purposes only. Always consult a medical professional.

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Tanishq77/skin-condition-classifier

🧠 Skin Condition Classifier (EfficientNetV2B0)

📌 Supported Skin Conditions

🧪 Model Performance

🏗️ Model Architecture

🧠 Usage

🔍 Load the model (Keras format)

🖼️ Input Format

📊 Output Format

🧾 License

👨‍💻 Author

Tanishq77/
skin-condition-classifier