Deep Learning Approach for Automated Skin Cancer Detection

Author: Durgesh Khade
Research Focus: Computer Vision & Medical Image Analysis

Abstract

This research presents a comprehensive deep learning framework for automated skin cancer detection using Convolutional Neural Networks (CNNs). The system implements a binary classification approach to distinguish between malignant and benign skin lesions, achieving reliable performance through strategic architectural design and data augmentation techniques.

Methodology

Model Architecture

The proposed CNN architecture employs a hierarchical feature extraction approach:

# CNN Architecture Implementation
model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)),
    BatchNormalization(),
    MaxPooling2D((2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Conv2D(128, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Conv2D(128, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Flatten(),
    Dropout(0.5),
    Dense(512, activation='relu'),
    Dense(1, activation='sigmoid')
])

Key Features:

• Input Resolution: 224×224 RGB images
• Feature Extraction: Progressive convolution layers (32→64→128→128 filters)
• Regularization: Batch normalization and dropout (0.5) for overfitting prevention
• Classification: Sigmoid activation for binary output (Cancer/Non-Cancer)

System Components

1. Model Training (model.py)

• Implements data augmentation strategies (shear, zoom, horizontal flip)
• Binary cross-entropy loss optimization using Adam optimizer
• Training/validation split with standardized preprocessing pipeline

2. Web Application (app.py)

• Streamlit-based user interface for clinical assessment
• Real-time image upload and prediction capabilities
• Probability scores for diagnostic confidence

3. Real-time Analysis (predictlocally.py)

• Live camera feed processing for immediate screening
• OpenCV integration for video capture and frame analysis
• Overlay visualization of prediction results

Dataset

Source: Skin Cancer Binary Classification Dataset

The dataset provides curated dermoscopic images with expert annotations for binary classification tasks, enabling robust model validation and performance assessment.

Technical Requirements

Dependencies

# Core ML Framework
tensorflow==2.7.0
keras==2.7.0

# Computer Vision
opencv-python-headless==4.5.5.62

# Web Interface
streamlit==1.2.0

Installation

# Clone repository
git clone <repository-url>
cd Skin-Cancer-Detection

# Install dependencies
pip install -r requirements.txt

Usage

Model Training

python model.py

Web Application Deployment

streamlit run app.py

Real-time Camera Analysis

python predictlocally.py

Research Implications

This work contributes to the growing field of computer-aided diagnosis in dermatology, offering:

• Accessibility: Deployment-ready solution for resource-constrained environments
• Efficiency: Real-time processing capabilities for immediate screening
• Scalability: Modular architecture supporting future enhancements

Performance Considerations

The model demonstrates robust generalization through:

• Strategic data augmentation preventing overfitting
• Batch normalization ensuring stable training convergence
• Dropout regularization improving model robustness

Future Work

Potential research directions include:

• Multi-class classification for specific cancer types
• Integration of attention mechanisms for lesion localization
• Deployment optimization for mobile platforms
• Clinical validation studies

Technical Notes

Development Environment: Linux-based implementation optimized for TensorFlow/Keras ecosystem
Image Processing: OpenCV-powered preprocessing and augmentation pipeline
Deployment: Streamlit framework enabling rapid prototyping and demonstration

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This research was conducted as part of ongoing work in medical image analysis and computer vision applications in healthcare.