CODEWITHSUNDEEP
tensorflowkerasimage-segmentationconv-neural-networkvgg-net
cappadavide
cappadavide

Reputation: 113

SegNet for CT images pretrained weights

I'm trying to train a SegNet for segmentation task on ct images (with Keras TF). I'm using VGG16 pretrained weights but I had a problem with the first convolutional layer because I'm using grayscale images but VGG was trained on rgb ones. I solved that using second method of this (can't use first method because requires too much memory). However it didn't help me, values are really bad (trained for 100 epochs).

Should I train the first convolutional layer from scratch?

Upvotes: 0

Views: 259

Answers (1)

B Douchet
B Douchet

Reputation: 1030

You can try to add a Conv2D before the vgg. Something like :

> Your Input(shape=(height,width,1))

Conv2D(filters=3,kernel_size=1, padding='same',activation='relu')

> The VGG pretrained network (input = (height,width,3))

is interesting in your case because 1x1 convolution is usually employed to change the depth of your object.

Upvotes: 1

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