Update README.md

README.md

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----
-license: mit
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+---
+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
+![DENnetwork](https://github.com/prazg/Deep-Features-Nifti-Network-DEN/assets/107046434/960b9016-a6d6-45d0-820d-73375297e4d8)
+
+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.
+
+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.
+
+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.
+
+5.	Model Compilation:
+•	The model is compiled with the Adam optimizer and uses categorical cross-entropy as the loss function.
+
+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.
+
+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.
+
+8.	Saving Extracted Features:
+•	The extracted features are saved as a NumPy array to a specified file path.
+
+9.	Input Shape:
+•	The model expects an input shape of 224×224×1, suitable for grayscale images of size 224x224 pixels.
+
+Inspiration:LeNet Architecture (25 layers)
+
+
+Copyright: Prajwal Ghimire