---
license: apache-2.0
library_name: keras
pipeline_tag: image-classification
tags:
- keras
- tensorflow
- efficientnet
- efficientnet-b3
- image-classification
- skin-disease
- medical-ai
---

# EfficientNet-B3 Skin Disease Classifier

## Overview

This repository provides an EfficientNet-B3 image classification model trained to recognize common skin diseases from clinical skin images.

The model is intended for research and educational purposes only and should not be used as a substitute for professional medical diagnosis.

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## Model Architecture

- Backbone: EfficientNet-B3
- Framework: TensorFlow / Keras
- Task: Multi-class Image Classification

---

## Disease Classes

The model predicts one of the following classes:

- Eczema
- ACD
- Psoriasis
- Tinea
- Urticaria
- Folliculitis
- Insect Bite
- Acne

---

## Dataset

The model was trained using a custom dataset constructed from the **SCIN (Skin Condition Image Network)** dataset and **DermNet** images. The collected images were manually curated and mapped into eight diagnostic categories.

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## Input

- RGB Image
- Image Size: 300 × 300 pixels

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## Output

The model returns the probability for each disease class.

Example:

| Disease   | Probability |
|-----------|-------------|
| Eczema    | 0.82        |
| Psoriasis | 0.10        |
| Tinea     | 0.04        |

---

## Files

| File                        | Description                  |
|-----------------------------|------------------------------|
| efficientnet_b3.keras       | Trained classification model |
| efficientnet_backbone.keras | EfficientNet backbone        |
| label_mapping.json          | Class index mapping          |
| training_config.json        | Training configuration       |

---

## Example

```python
import tensorflow as tf

model = tf.keras.models.load_model("efficientnet_b3.keras")
```

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## Intended Use

This model is designed for:

- Academic research
- Computer Vision experiments
- Medical AI education
- Prototype applications

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## Limitations

- Not intended for clinical diagnosis.
- Performance depends on image quality.
- Predictions should always be interpreted by healthcare professionals.

---

## Author

**Chantaro Ntw**

AI Engineer | Computer Vision | Medical AI