Enhancing CNN to Improve Skin Cancer Prediction

Screeshots

Model Outcomes

Model With 10 Epochs

Classification Report

CNN Training Model

Testing Accuracy

Image Augmentation

Description

Improving CNN's ability to forecast skin cancer requires an in-depth understanding of artificial intelligence and machine learning. By leveraging these technologies, we can significantly enhance the prediction model's efficiency and accuracy in identifying skin cancer. This project explores the integration of AI, machine learning, and CNN to develop a robust skin cancer prediction prototype.

Key Features

• Artificial Intelligence (AI)
• Machine Learning
• Convolutional Neural Networks (CNN)
• Comprehensive Planning

Frameworks

• TensorFlow and Keras
• Python
• Google Colab

What I Learned

• AI and Machine Learning Integration: Gained insights into integrating AI and machine learning to enhance medical prediction models.

• CNN Architecture: Learned advanced techniques in CNN architecture design and optimization for medical imaging.

• Project Planning and Iteration: Understood the importance of iterative project planning and reflection for continuous improvement.

Deployment

• Deploying the enhanced CNN model for skin cancer prediction is streamlined through Google Colab. Follow these steps to get started:

• Prerequisites

• Ensure you have a Google account to access Google Colab.

• Basic understanding of Python and machine learning concepts.

Steps to Deploy the Model

1. Access the Google Colab Notebook

2. Open the Google Colab Notebook here. Clone the Repository

• In the first cell of the Colab notebook, run the following command to clone the project repository:

!git clone https://github.com/IzzyDevOps/Skin-Cancer-Prediction %cd SkinCancerPrediction

3. Install Dependencies

• Install the required Python libraries by running:

!pip install -r requirements.txt

• Set Up Google Drive

• If you need to access additional data or save results, mount your Google Drive:

from google.colab import drive drive.mount('/content/drive')

• Run the Model

4. Execute the entire Colab notebook to train and test the model:

• Click on "Runtime" → "Run all" to run all cells sequentially.

• Follow the instructions in the notebook for model training and evaluation.

• View Results

5. After the notebook completes execution, check the results and model performance metrics in the Colab environment.

• Download or visualize the results directly from the Colab interface.

Author Info

• Name: Kaone Keboetseng

• For any inquiries or further information, you can contact me via email at [email protected].

Conclusion

My research demonstrates that enhancing the CNN model significantly increases the prediction accuracy for skin cancer. The improved model offers promising advancements in dermatological diagnosis, potentially leading to earlier detection and better patient outcomes.