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---
license: apache-2.0
---
# 🦠 Melanoma Detection Model
## 📄 Overview
🤖 **Model Name:** Melanoma Detection Model
🧠 **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://example.com/melanoma_detection_model/download) to download
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## 📚 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.
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## 🔍 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.
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## 📈 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.
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## 🛠️ 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
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## 📥 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.')
```
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## ⚠️ 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)
--- |