Use Deep Learning model to diagnose 14 pathologies on Chest X-Ray and use GradCAM Model Interpretation Method
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Oct 12, 2020 - Jupyter Notebook
Use Deep Learning model to diagnose 14 pathologies on Chest X-Ray and use GradCAM Model Interpretation Method
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.
This repo is special for those who want to start learning computer vision related tasks such as image classification.
Based on the mmdetection framework, compute various salience maps for object detection.
Custom Keras Callbacks for Feature Visualization, Class Activation Map, Grad-CAM
code for studying OpenAI's CLIP explainability
Saliency Enhancing with Scaling and Sliding
Paper under review on "Multimedia Tools and Applications" journal.
Making CNNs interpretable.
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.
Keras implementation for GradCAM analysis for dual 3D CNN model.
This repository consists of models of CNN for classifying different types of charts. Moreover, it also includes script of fine-tuned VGG16 for this task. On top of that CradCAM implementation of fine-tuned VGG16.
Easy to follow GradCAM visualization - Google collab notebooks where you just have to upload the image and mention the target class to get the feature visualization for models trained on COCO and Imagenet dataset.
CNN architectures Resnet-50 and InceptionV3 have been used to detect whether the CT scan images is covid affected or not and prediction is validated using explainable AI frameworks LIME and GradCAM.
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.
This repository aims to implement a mushroom type classifier using PyTorch, utilizing various models to enhance performance. Additionally, the project includes an analysis of the model's performance using Gradient-Class Activation Map (Grad-CAM) visualization.
A pytorch implementation of GradCAM
FellowshipAi project
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