https://github.com/phrugsa-limbunlom/gradcam
This repository implements GradCAM technique to localize the fracture's location on X-ray image.
https://github.com/phrugsa-limbunlom/gradcam
gradcam-heatmap
Last synced: about 2 months ago
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This repository implements GradCAM technique to localize the fracture's location on X-ray image.
- Host: GitHub
- URL: https://github.com/phrugsa-limbunlom/gradcam
- Owner: phrugsa-limbunlom
- License: mit
- Created: 2020-06-20T17:20:41.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2025-02-21T19:05:03.000Z (3 months ago)
- Last Synced: 2025-02-21T20:19:36.877Z (3 months ago)
- Topics: gradcam-heatmap
- Language: Jupyter Notebook
- Homepage:
- Size: 581 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
GradCam
Gradient-weighted Class Activation Mapping (Grad-CAM) is a technique for visualizing the important regions in an image that contribute to the prediction of a convolutional neural network. It uses the gradients of the target class flowing into the final convolutional layer to generate a coarse localization map.
# Apply GradCAM technique on input images
**Input**: Original X-ray image of mandible bones
**Output**: The highlighted X-ray image with heatmap indicating the location of the fracture
```python
grad_model = tf.keras.models.Model([model.inputs], [model.get_layer('bn').output, model.output])
with tf.GradientTape() as tape:
conv_outputs, predictions = grad_model(np.array([img]))
loss = predictions[:,CLASS_INDEX]
output = conv_outputs[0]
grads = tape.gradient(loss, conv_outputs)[0]
guided_grads = tf.cast(output > 0, 'float32') * tf.cast(grads >0, 'float32') * grads
weights = tf.reduce_mean(guided_grads, axis=(0, 1))
cam = np.ones(output.shape[0: 2], dtype = np.float32)
for i, w in enumerate(weights):
cam += w * output[:, :, i]
cam = cv2.resize(cam.numpy(), (512, 512))
cam = np.maximum(cam, 0)
heatmap = (cam - cam.min()) / (cam.max() - cam.min())
cam = cv2.applyColorMap(np.uint8(255*heatmap), cv2.COLORMAP_JET)
output_image = cv2.addWeighted(cv2.cvtColor(img.astype('uint8'), cv2.COLOR_RGB2BGR), 0.8, cam, 0.5, 0.5)
img_resize = cv2.resize(output_image, (1400,800))
```
**Parameter**
- CLASS_INDEX : An index determines the fracture class name of mandible bones
**Input Example**
```python
img = cv2.imread(r'PATH OF THE IMAGE',0)
plt.imshow(img)
```
**Output Example**
```python
plt.imshow(img_resize)
```
