Skin Cancer Classification using Deep Learning

A deep learning-based approach for skin cancer detection using the ISIC dataset and data augmentation techniques to improve model performance.

Table of Contents

• General Information
• Dataset and Preprocessing
• Data Augmentation
• Model Architecture
• Training and Results
• Technologies Used
• Conclusions
• Acknowledgements
• Contact

General Information

• This project aims to classify different types of skin cancer using deep learning.
• It addresses the issue of class imbalance and improves model generalization using data augmentation.
• The dataset used is the ISIC (International Skin Imaging Collaboration) dataset.
• The model is trained using TensorFlow and Keras with techniques like dropout and data augmentation.

Dataset and Preprocessing

• The dataset consists of multiple skin lesion classes, and initially, there was an imbalance among classes.
• To resolve class imbalance, the Augmentor library was used to generate synthetic images.
• The dataset was split into training and validation sets, ensuring one-hot encoding for multi-class classification.

Data Augmentation

• Used Augmentor to generate 500 additional samples per class to balance the dataset.
• Applied transformations such as:
  • Rotation (±10 degrees)
  • Flipping
  • Scaling
• Ensured that augmented data was properly integrated into the TensorFlow dataset pipeline.

Model Architecture

• CNN-based model with the following layers:
  • Convolutional layers with ReLU activation
  • Batch normalization
  • Dropout layers to prevent overfitting
  • Fully connected layers for classification
• Used categorical cross-entropy as the loss function due to multi-class classification.

Training and Results

• Epochs: 20
• Optimizer: Adam
• Loss Function: Categorical Cross-Entropy
• Training Accuracy: Improved from ~20% to ~55% after augmentation and dropout adjustments.
• Validation Accuracy: Increased but still required fine-tuning to improve generalization.
• Checked for signs of underfitting/overfitting and made necessary adjustments.

Technologies Used

• Python 3.x
• TensorFlow/Keras
• Augmentor
• NumPy
• Pandas
• Matplotlib
• Glob

Conclusions

• Data augmentation significantly improved class balance, leading to better generalization.
• Dropout layers helped prevent overfitting, stabilizing the validation accuracy.
• Further improvements can be made using transfer learning with pre-trained models (e.g., ResNet, EfficientNet).

Acknowledgements

• Inspired by the ISIC Skin Cancer Challenge.
• References from TensorFlow and Keras documentation.
• Augmentor library documentation for handling class imbalance.

Contact

Created by [@AnishRane-cox] - feel free to reach out!