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README.md

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-license: apache-2.0
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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
+
+---
+
+## 📚 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)
+
+---