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Melanoma Skin Cancer Detection using CNN

Melanoma skin cancer detection involves identifying and classifying melanoma, a type of skin cancer that develops from melanocytes, the cells that produce melanin. Melanoma is known for its potential to metastasize and become life-threatening if not detected and treated early.

Table of Contents

  1. Overview
  2. Technologies Used
  3. Data Preparation
  4. Model Building and Training
  5. Model Evaluation
  6. Conclusions
  7. Acknowledgements
  8. Contact

Overview

Melanoma skin cancer detection involves the identification and classification of melanoma, a type of skin cancer that develops from melanocytes, the cells that produce melanin. Melanoma is known for its potential to metastasize and become life-threatening if not detected and treated early. This project aims to develop a system for the early detection and classification of melanoma skin cancer using Convolutional Neural Networks (CNN).

Project Objectives

  • Early Detection: Develop an automated system to assist dermatologists in the early detection and classification of melanoma lesions.
  • Data-Driven Insights: Utilize machine learning models trained on large datasets of skin lesion images to improve diagnostic accuracy.
  • Optimize Model Performance: Fine-tune the CNN model for accurate and efficient predictions.

Technologies Used

  • Python: 3.12.2
  • Jupyter Notebook: 7.4.2
  • Anaconda: 2023.10
  • Numpy: 1.26.2
  • Pandas: 2.1.4
  • Plotly: 5.18.0
  • Matplotlib: 3.6.2
  • Git: 2.42.1
  • Seaborn: 0.12.2
  • Keras: 2.15.x
  • TensorFlow: 2.15.x

Data Preparation

  • Data Importation: Import image data and convert it into a suitable dataset format for model training.
  • Data Preprocessing: Apply normalization and augmentation techniques to enhance model performance and generalization.
  • Data Visualization: Visualize the data to uncover patterns and insights, aiding in better understanding and preprocessing.

Model Building and Training

  • Model Creation: Build a CNN model with multiple convolutional layers to extract features from images.
  • Model Compilation: Compile the model using appropriate loss functions, optimizers, and evaluation metrics.
  • Model Training: Train the CNN model using the training dataset and validate it using a validation dataset. Implement early stopping and checkpointing to optimize training.

Model Evaluation

  • Performance Metrics: Evaluate the model using accuracy, precision, recall, F1-score, and AUC-ROC metrics.
  • Residual Analysis: Perform residual analysis to validate model assumptions and ensure robustness.
  • Test Predictions: Use the final model to make predictions on the test dataset and assess its performance.

Conclusions

  • Efficiently reshaped and preprocessed the data for model training.
  • Visualized data to uncover significant patterns and insights.
  • Built and trained a robust CNN model for melanoma classification.
  • Achieved high accuracy and reliability in classifying melanoma lesions from images.
  • Demonstrated the potential of automated systems in aiding early detection and improving diagnostic outcomes.

Acknowledgements

Contact

Created by @SandeepGitGuy - Feel free to contact me!