Melanoma Detection Using Deep Learning Techniques

Melanoma Detection Using Deep Learning Techniques

🩺 Melanoma Detection using Deep Learning

Overview

This project applies deep learning techniques to assist in the early detection of melanoma, a type of skin cancer, through automated analysis of dermoscopic images. By leveraging a lightweight convolutional neural network (CNN), the system classifies skin lesions as either benign or malignant, supporting dermatologists in diagnostic decision-making.

The system was trained and evaluated on publicly available medical imaging datasets and deployed with an intuitive web interface for real-time use.

🧰 Key Features

  • Classification of skin lesion images into benign or malignant classes.
  • Utilizes pre-trained MobileNetV2 architecture for efficient and accurate inference.
  • Data augmentation pipeline to enhance model robustness.
  • Visualizations of model predictions for interpretability.
  • Deployed as an accessible web application for users to upload images and receive immediate predictions.

🔷 Approach & Techniques

  • Data Collection: Public dermoscopic image datasets (e.g., ISIC Archive).
  • Preprocessing: Resizing, normalization, augmentation (rotation, flipping, zoom).
  • Model:
    • Base: MobileNetV2 pretrained on ImageNet.
    • Modified with custom dense layers for binary classification.
    • Fine-tuned on melanoma dataset for transfer learning.
  • Training:
    • Loss: Binary Cross-Entropy
    • Optimizer: Adam
    • Metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC
  • Deployment:
    • Web app built with Gradio and hosted on Hugging Face Spaces.

🚀 Tech Stack

  • Language: Python
  • Frameworks/Libraries:
    • TensorFlow / Keras
    • OpenCV
    • scikit-learn
    • Gradio
    • Matplotlib, Seaborn (for visualization)
  • Deployment:
    • Hugging Face Spaces

📊 Impact

This project demonstrates how transfer learning with lightweight CNN architectures can provide fast, accurate predictions for critical healthcare tasks. The deployment as a web app ensures accessibility, making it a practical aid for early melanoma detection, which is crucial for improving patient outcomes.

🌐 Demo

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