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gradcam-visualization

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Used the Functional API to built custom layers and non-sequential model types in TensorFlow, performed object detection, image segmentation, and interpretation of convolutions. Used generative deep learning including Auto Encoding, VAEs, and GANs to create new content.

  • Updated Jun 9, 2021
  • Jupyter Notebook

Three different DNN models Xception, In- ceptionV3, and VGG19 were used for the classification of crop disease from the image dataset, and explainable AI XAI was used to evaluate their performance. InceptionV3 was achieved as the best model with the highest accuracy of 97.20% accuracy.

  • Updated Aug 23, 2023
  • Jupyter Notebook

A project for lung disease detection and analysis using deep learning. It includes lung segmentation, disease classification, and severity localization with Grad-CAM for visual explanations. This repository provides code, datasets, and documentation for replication and further research.

  • Updated Jun 20, 2024
  • Jupyter Notebook

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