README.md

metadata

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 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

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

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🛠️ Technical Details

• Based on: MobileNetV2
• 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)

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📤 Output Format

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

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🧩 Model Training

• Dataset: 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

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💡 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.

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.

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.

# 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.

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.

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👥 Contributors

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

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📝 References

• Skin Cancer Dataset: Link
• MobileNetV2: Link

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