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import numpy as np
import gradio as gr
from huggingface_hub import from_pretrained_keras
def read_nifti_file(filepath):
"""Read and load volume"""
# Read file
scan = nib.load(filepath)
# Get raw data
scan = scan.get_fdata()
return scan
def normalize(volume):
"""Normalize the volume"""
min = -1000
max = 400
volume[volume < min] = min
volume[volume > max] = max
volume = (volume - min) / (max - min)
volume = volume.astype("float32")
return volume
def resize_volume(img):
"""Resize across z-axis"""
# Set the desired depth
desired_depth = 64
desired_width = 128
desired_height = 128
# Get current depth
current_depth = img.shape[-1]
current_width = img.shape[0]
current_height = img.shape[1]
# Compute depth factor
depth = current_depth / desired_depth
width = current_width / desired_width
height = current_height / desired_height
depth_factor = 1 / depth
width_factor = 1 / width
height_factor = 1 / height
# Rotate
img = ndimage.rotate(img, 90, reshape=False)
# Resize across z-axis
img = ndimage.zoom(img, (width_factor, height_factor, depth_factor), order=1)
return img
def process_scan(path):
"""Read and resize volume"""
# Read scan
volume = read_nifti_file(path)
# Normalize
volume = normalize(volume)
# Resize width, height and depth
volume = resize_volume(volume)
return volume
def infer(filename):
vol = process_scan(filename.name)
vol = np.expand_dims(vol, axis=0)
prediction = model.predict(vol)[0]
scores = [1 - prediction[0], prediction[0]]
class_names = ["normal", "abnormal"]
result = []
for score, name in zip(scores, class_names):
result = result + [f"This model is {(100 * score):.2f} percent confident that CT scan is {name}"]
return result
model = from_pretrained_keras('jalFaizy/3D_CNN')
filepath = gr.inputs.File()
text = gr.outputs.Textbox()
iface = gr.Interface(
infer,
filepath,
text,
title='3D CNN for CT scans',
examples=['example_1_normal.nii.gz']
)
iface.launch() |