---
license: apache-2.0
base_model:
- google/mobilenet_v2_1.0_224
pipeline_tag: image-classification
---
# 🦠 Melanoma Detection Model

## 📄 Overview

🤖 **Model Name:** MDM-3

🧠 **Model Type:** Convolutional Neural Network (CNN)

📊 **Input:** 224x224 RGB images of skin lesions

🔢 **Output:** A binary classification (Melanoma or Not Melanoma)

🎯 **Purpose:** To identify and classify skin lesions as melanoma or non-melanoma with high accuracy

☁️ **Download:** Click [here](https://huggingface.co/lizardwine/Melanoma-003/resolve/main/Melanoma-003.keras) to download

---

## 📚 Description

This model is designed to detect melanoma from skin lesion images. It processes input images of size 224x224 pixels and outputs a binary classification indicating whether the lesion is melanoma or not. The model is trained to differentiate between malignant melanoma and benign conditions.

---

## 🔍 Use Cases

1. **Medical Diagnosis:** 🏥 Assisting dermatologists in diagnosing melanoma from images.
2. **Skin Cancer Screening:** 🩺 Enhancing early detection efforts in large-scale skin cancer screening programs.
3. **Patient Monitoring:** 👩‍⚕️ Helping in monitoring patients with a history of melanoma or high risk.

---

## 📈 Performance

🔍 **Accuracy:** ~99% on the [Skin Cancer Dataset](https://www.kaggle.com/datasets/shashanks1202/skin-cancer-dataset)

🕒 **Latency:** Suitable for real-time analysis in clinical settings.

---

## 🛠️ Technical Details
- **Based on:** [MobileNetV2](https://www.kaggle.com/models/google/mobilenet-v2)
- **Architecture:** Convolutional Neural Network (CNN)
- **Layers:** Convolutional layers, pooling layers, fully connected layers
- **Activation Functions:** ReLU, Sigmoid

---

## 📥 Input Format

- **Type:** RGB image
- **Shape:** 224x224 pixels
- **Range:** 0-1 (pixel intensity)

---

## 📤 Output Format

- **Type:** Binary classification
- **Shape:** Scalar value
- **Range:** 0 (Not Melanoma), 1 (Melanoma)

---

## 🧩 Model Training
- **Dataset:** [Skin Cancer Dataset](https://www.kaggle.com/datasets/shashanks1202/skin-cancer-dataset) 📚
- **First step:**
  - **Training Epochs:** 50
  - **Batch Size:** 32
  - **Optimizer:** Adam
  - **Learning rate:** 1e-5
  - **Layers:** Only last layer
- **Second step:**
  - **Training Epochs:** 5
  - **Batch Size:** 32
  - **Optimizer:** Adam
  - **Learning rate:** 1e-5
  - **Layers:** All layers
---

## 💡 How to Use

1. **Preprocess the Image:** Resize and normalize the image to 224x224 pixels with pixel values between 0 and 1.
2. **Feed the Image:** Input the preprocessed image into the model.
3. **Interpret the Output:** Analyze the output to determine if the lesion is melanoma or not.

### Loading the Model

To use the model, first, load it using your preferred framework.

```python
import tensorflow as tf

# Load the pre-trained model
model = tf.keras.models.load_model('path/to/Melanoma-003.keras')
```

### Preprocessing the Input

Preprocess the input image to fit the model's requirements.

```python
import numpy as np
from tensorflow.keras.preprocessing import image

def preprocess_image(img_path):
    # Load the image
    img = image.load_img(img_path, target_size=(224, 224))
    # Convert to numpy array
    img_array = image.img_to_array(img)
    # Normalize the image
    img_array = img_array / 255.0
    # Reshape to add batch dimension
    img_array = np.expand_dims(img_array, axis=0)
    return img_array

# Example usage
img_path = 'path/to/your/image.jpg'
processed_image = preprocess_image(img_path)
```

### Making Predictions

Use the model to predict if the lesion is melanoma.

```python
# Predict the class
prediction = model.predict(processed_image)

# Interpret the result
if prediction[0] > 0.5:
    print('The lesion is classified as Melanoma.')
else:
    print('The lesion is classified as Not Melanoma.')
```

### Full Example

Combining all steps into a single example.

```python
import tensorflow as tf
import numpy as np
from tensorflow.keras.preprocessing import image

# Load the pre-trained model
model = tf.keras.models.load_model('path/to/MelanomaDetectionModel.h5')

def preprocess_image(img_path):
    img = image.load_img(img_path, target_size=(224, 224))
    img_array = image.img_to_array(img)
    img_array = img_array / 255.0
    img_array = np.expand_dims(img_array, axis=0)
    return img_array

img_path = 'path/to/your/image.jpg'
processed_image = preprocess_image(img_path)

prediction = model.predict(processed_image)
if prediction[0] > 0.5:
    print('The lesion is classified as Melanoma.')
else:
    print('The lesion is classified as Not Melanoma.')
```

---

## ⚠️ Limitations

- **Image Quality:** Performance may be affected by poor-quality or low-resolution images.
- **Generalization:** Model performance may vary with images not represented in the training data.

---

## 👥 Contributors

- **Developer:** Lizardwine (x@lizardwine.com)
- **Organization:** lizardwine
- **Date:** 05/09/2024

---

## 📝 References

- Skin Cancer Dataset: [Link](https://www.kaggle.com/datasets/shashanks1202/skin-cancer-dataset)
- MobileNetV2: [Link](https://www.kaggle.com/models/google/mobilenet-v2)

---