| --- |
| license: mit |
| language: |
| - en |
| pipeline_tag: image-feature-extraction |
| tags: |
| - deep |
| - features |
| - CNN |
| - latent |
| - glioblastoma |
| --- |
| # Deep Extraction Network (DEN) |
| This repository defines a sequential Convolutional Neural Network (CNN) model (18 layers) that extracts deep features from Nifti MRI files of segmented glioblastoma tumours |
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| Here are the key characteristics of this CNN model: |
| 1. Architecture Overview: |
| • The model is a sequential model, meaning it has a linear stack of layers. |
| • It consists of five convolutional layers, followed by a flattening operation and a fully connected layer. |
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| 3. Convolutional Layers: |
| • First Layer: It has 96 filters, each of size 9×99×9, with a stride of 4×44×4 and 'valid' padding. This is followed by ReLU activation, batch normalization, and max pooling with a 3×33×3 window and 2×22×2 stride. |
| • Second Layer: It has 256 filters of size 7×77×7, stride 1×11×1, and 'same' padding, followed by ReLU activation, batch normalization, and max pooling. |
| • Third and Fourth Layers: Each has 384 filters of size 3×33×3 with a stride of 1×11×1 and 'same' padding, followed by ReLU activation. |
| • Fifth Layer: It has 256 filters, similar to the third and fourth layers, and is followed by ReLU activation and max pooling. |
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| 4. Fully Connected Layer: |
| • After the convolutional and pooling layers, the model flattens the output and feeds it into a dense layer with 4096 units, followed by ReLU activation. |
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| 5. Model Compilation: |
| • The model is compiled with the Adam optimizer and uses categorical cross-entropy as the loss function. |
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| 6. Data Preprocessing for Medical Imaging: |
| • The code includes steps for loading and preprocessing medical images (NIfTI format). It involves resizing, normalizing, and expanding dimensions to match the input shape required by the CNN model. |
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| 7. Feature Extraction and Visualization: |
| • The model is used to predict features from preprocessed medical images. These features are then reshaped and visualized using a heatmap. |
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| 8. Saving Extracted Features: |
| • The extracted features are saved as a NumPy array to a specified file path. |
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| 9. Input Shape: |
| • The model expects an input shape of 224×224×1, suitable for grayscale images of size 224x224 pixels. |
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| Inspiration:LeNet Architecture (25 layers) |
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| Copyright: Prajwal Ghimire |
