skin-condition-classifier

🧠 Skin Condition Classifier (EfficientNetV2B0)

This model classifies facial skin images into 6 common dermatological conditions using a fine-tuned EfficientNetV2B0 architecture.

📌 Supported Skin Conditions

• Acne
• Carcinoma
• Eczema
• Keratosis
• Milia
• Rosacea

🧪 Model Performance

• 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)

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

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.

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🧾 License

MIT License

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👨‍💻 Author

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

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

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