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grad-cam-visualization

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We will build and train a Deep Convolutional Neural Network (CNN) with Residual Blocks to detect the type of scenery in an image. In addition, we will also use a technique known as Gradient-Weighted Class Activation Mapping (Grad-CAM) to visualize the regions of the inputs and help us explain how our CNN models think and make decision.

  • Updated Sep 10, 2021
  • Jupyter Notebook

AI-powered skin disease classification system using a fine-tuned ResNet CNN with Grad-CAM explainability. Built with PyTorch and Streamlit to predict 8 skin conditions and visualize model attention for transparent, confidence-aware predictions.

  • Updated Jan 27, 2026
  • Jupyter Notebook

A deep learning-powered medical diagnosis system that detects Pneumonia from chest X-rays and Brain Tumors from MRI scans using two trained CNN models. Includes a FastAPI backend and Streamlit UI for real-time predictions. Built for practical deployment with explainability (Grad-CAM) and modular architecture.

  • Updated Jul 12, 2025
  • Jupyter Notebook

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