In this project I have build algorithm for deducting pneumonia using chest X-ray. The dataset for this project was curated by the NIH specifically to address the problem of a lack of large x-ray datasets with ground truth labels to be used in the creation of disease detection algorithms.
my model weights seems to be giving some dimensional errors if i parse it with your script wich i think is due to the image processing function.please can you help with the adjustment of the functionality to fit the script requrements
the script was actually a flask web application but trying to intergrate its weights with your script (infrence.ipynb) inoder to process multiple dicom files like what you are doing in infrence.ipynb.let me copy the code beow so you can see how and why an erro could pop up and a possible fix. Thanks in advance
#::: Import modules and packages :::
from flask import Flask, redirect, url_for, request, render_template
from werkzeug.utils import secure_filename
from gevent.pywsgi import WSGIServer
from tensorflow.keras.models import model_from_json
from tensorflow.python.framework import ops
ops.reset_default_graph()
from keras.preprocessing import image
import numpy as np
import h5py
from PIL import Image
import PIL
import os
#::: Flask App Engine :::
app = Flask(name)
MODEL_ARCHITECTURE = './model/model_adam.json'
MODEL_WEIGHTS = './model/model_100_eopchs_adam_20190807.h5'
json_file = open(MODEL_ARCHITECTURE)
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
model.load_weights(MODEL_WEIGHTS)
print('Model loaded. Check http://127.0.0.1:5000/')
def model_predict(img_path, model):
'''
Args:
-- img_path : an URL path where a given image is stored.
-- model : a given Keras CNN model.
'''
IMG = image.load_img(img_path).convert('L')
print(type(IMG))
# Pre-processing the image
IMG_ = IMG.resize((257, 342))
print(type(IMG_))
IMG_ = np.asarray(IMG_)
print(IMG_.shape)
IMG_ = np.true_divide(IMG_, 255)
IMG_ = IMG_.reshape(1, 342, 257, 1)
print(type(IMG_), IMG_.shape)
print(model)
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='rmsprop')
predict_x = model.predict(IMG_)
prediction = np.argmax(predict_x,axis=1)
return prediction
@app.route('/', methods=['GET'])
def index():
# Main Page
return render_template('index.html')
@app.route('/predict', methods=['GET', 'POST'])
def upload():
# Constants:
classes = {'TRAIN': ['BACTERIA', 'NORMAL', 'VIRUS'],
'VALIDATION': ['BACTERIA', 'NORMAL'],
'TEST': ['BACTERIA', 'NORMAL', 'VIRUS']}
if request.method == 'POST':
# Get the file from post request
f = request.files['file']
# Save the file to ./uploads
basepath = os.path.dirname(__file__)
file_path = os.path.join(
basepath, 'uploads', secure_filename(f.filename))
f.save(file_path)
# Make a prediction
prediction = model_predict(file_path, model)
predicted_class = classes['TRAIN'][prediction[0]]
print('We think that is {}.'.format(predicted_class.lower()))
return str(predicted_class).lower()
if name == 'main':
app.run(debug = True)
Below is the order of how I am going to present my problem:
So to achieve diagnosis I am trying to integrate my app.py script which takes in jpegs, with your Pneumonia binary classification which takes in dicom files so as to take advantage of the dicom processing function of the your inference.ipynb script but using all of the information and weights of my Viral and Bacterial script, so that it can be used in a clinical setup. Clinical setups use dicom files not jpegs, that is why I am trying to combine these two scripts to reach the goal.
Below is my app.py script for Viral and Bacterial Pneumonia Classification which takes in jpegs, which I am trying to integrate with your inference script that I am also going to attach further below:
#::: Import modules and packages :::
# Flask utils
from flask import Flask, redirect, url_for, request, render_template
from werkzeug.utils import secure_filename
from gevent.pywsgi import WSGIServer
# Import Keras dependencies
from tensorflow.keras.models import model_from_json
from tensorflow.python.framework import ops
ops.reset_default_graph()
from keras.preprocessing import image
# Import other dependecies
import numpy as np
import h5py
from PIL import Image
import PIL
import os
#::: Flask App Engine :::
# Define a Flask app
app = Flask(__name__)
# ::: Prepare Keras Model :::
# Model files
MODEL_ARCHITECTURE = './model/model_adam.json'
MODEL_WEIGHTS = './model/model_100_eopchs_adam_20190807.h5'
# Load the model from external files
json_file = open(MODEL_ARCHITECTURE)
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
# Get weights into the model
model.load_weights(MODEL_WEIGHTS)
print('Model loaded. Check http://127.0.0.1:5000/')
# ::: MODEL FUNCTIONS :::
def model_predict(img_path, model):
'''
Args:
-- img_path : an URL path where a given image is stored.
-- model : a given Keras CNN model.
'''
IMG = image.load_img(img_path).convert('L')
print(type(IMG))
# Pre-processing the image
IMG_ = IMG.resize((257, 342))
print(type(IMG_))
IMG_ = np.asarray(IMG_)
print(IMG_.shape)
IMG_ = np.true_divide(IMG_, 255)
IMG_ = IMG_.reshape(1, 342, 257, 1)
print(type(IMG_), IMG_.shape)
print(model)
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='rmsprop')
predict_x = model.predict(IMG_)
print(predict_x)
prediction = np.argmax(predict_x,axis=1)
print(prediction)
return prediction
# ::: FLASK ROUTES
@app.route('/', methods=['GET'])
def index():
# Main Page
return render_template('index.html')
@app.route('/predict', methods=['GET', 'POST'])
def upload():
# Constants:
classes = {'TRAIN': ['BACTERIA', 'NORMAL', 'VIRUS'],
'VALIDATION': ['BACTERIA', 'NORMAL'],
'TEST': ['BACTERIA', 'NORMAL', 'VIRUS']}
if request.method == 'POST':
# Get the file from post request
f = request.files['file']
# Save the file to ./uploads
basepath = os.path.dirname(__file__)
file_path = os.path.join(
basepath, 'uploads', secure_filename(f.filename))
f.save(file_path)
# Make a prediction
prediction = model_predict(file_path, model)
predicted_class = classes['TRAIN'][prediction[0]]
print('We think that is {}.'.format(predicted_class.lower()))
return str(predicted_class).lower()
if __name__ == '__main__':
app.run(debug = True)`
Below again is your script for Pneumonia binary classification which is taking in dicom files that I am trying to integrate with the weights and preprocessing information of my Viral and Bacterial classifier that I want to use:
## Loading standard modules and libraries
import numpy as np
import pandas as pd
import pydicom
%matplotlib inline
import matplotlib.pyplot as plt
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.models import model_from_json
from skimage.transform import resize
# This function reads in a .dcm file, checks the important fields for our device, and returns a numpy array
# of just the imaging data
def check_dicom(filename):
print('Loading file {} ...'.format(filename))
ds = pydicom.dcmread(filename)
if (ds.BodyPartExamined !='CHEST') | (ds.Modality !='DX') | (ds.PatientPosition not in ['PA', 'AP']):
print('The image is not valid because the image position, the image type or the body part is not as per standards')
return
else:
print('ID:', ds.PatientID,
'Age:', ds.PatientAge,
'Modality:', ds.Modality,
'Postion: ', ds.PatientPosition,
'Body Part: ', ds.BodyPartExamined,
'Study Desc: ', ds.StudyDescription)
img = ds.pixel_array
return img
# This function takes the numpy array output by check_dicom and
# runs the appropriate pre-processing needed for our model input
def preprocess_image(img,img_mean,img_std,img_size):
# todo
img = resize(img, (224,224))
img = img / 255.0
grey_img = (img - img_mean) / img_std
proc_img = np.zeros((224,224,3))
proc_img[:, :, 0] = grey_img
proc_img[:, :, 1] = grey_img
proc_img[:, :, 2] = grey_img
proc_img = np.resize(proc_img, img_size)
return proc_img
# This function loads in our trained model w/ weights and compiles it
def load_model(model_path, weight_path):
# todo
json_file = open(model_path, 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
model.load_weights(weight_path)
return model
# This function uses our device's threshold parameters to predict whether or not
# the image shows the presence of pneumonia using our trained model
def predict_image(model, img, thresh):
# todo
result = model.predict(img)
print('Predicted value:', result)
predict=result[0]
prediction = "Negative"
if(predict > thresh):
prediction = "Positive"
return prediction
# This function uses our device's threshold parameters to predict whether or not
# the image shows the presence of pneumonia using our trained model
def predict_image(model, img, thresh):
# todo
result = model.predict(img)
print('Predicted value:', result)
predict=result[0]
prediction = "Negative"
if(predict > thresh):
prediction = "Positive"
return prediction
test_dicoms = ['test1.dcm','test2.dcm','test3.dcm','test4.dcm','test5.dcm','test6.dcm']
model_path = "my_model2.json" #path to saved model
weight_path = "xray_class_my_model2.best.hdf5" #path to saved best weights
IMG_SIZE=(1,224,224,3) # This might be different if you did not use vgg16
img_mean = 0.49262813 # mean image value from Build and train model line 22
img_std = 0.24496286 # loads the std dev from Build and train model line 22
my_model = load_model(model_path, weight_path) #loads model
thresh = 0.62786263 #threshold value for New Model2 from Build and train model line 66 at 80% Precision
# use the .dcm files to test your prediction
for i in test_dicoms:
img = np.array([])
img = check_dicom(i)
if img is None:
continue
img_proc = preprocess_image(img,img_mean,img_std,IMG_SIZE)
pred = predict_image(my_model,img_proc,thresh)
print('Model Classification:', pred , 'for Pneumonia' )
print('--------------------------------------------------------------------------------------------------------')
Below is what I have tried so far using your script but the diagnosis I am getting is always Viral on each and every dicom sample when I have integrated my Viral and Bacterial Pneumonia classifier with you script:
## Loading standard modules and libraries
import numpy as np
import pandas as pd
import pydicom
from PIL import Image
#%matplotlib inline
import matplotlib.pyplot as plt
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.models import model_from_json
from keras.preprocessing import image
from skimage.transform import resize
# This function reads in a .dcm file, checks the important fields for our device, and returns a numpy array
# of just the imaging data
def check_dicom(filename):
print('Loading file {} ...'.format(filename))
ds = pydicom.dcmread(filename)
if (ds.BodyPartExamined !='CHEST'): #| (ds.Modality !='DX'): #| (ds.PatientPosition not in ['PA', 'AP']):
print('The image is not valid because the image position, the image type or the body part is not as per standards')
return
else:
print('ID:', ds.PatientID,
'Age:', ds.PatientAge,
'Modality:', ds.Modality,
'Postion: ', ds.PatientPosition,
'Body Part: ', ds.BodyPartExamined,
'Study Desc: ', ds.StudyDescription)
img = ds.pixel_array
return img
# This function takes the numpy array output by check_dicom and
# runs the appropriate pre-processing needed for our model input
def preprocess_image(img):
# todo
#im = np.reshape(img, (342,257 ))
#im = np.arange(257)
#img = Image.fromarray(im)
#img = image.load_img(img).convert('L')
img = resize(img, (342,257))
grey_img = img / 255.0
#grey_img = (img - img_mean) / img_std
proc_img = np.zeros((1,342,257,1))
proc_img[:, :, :, 0] = grey_img
#proc_img[:, :, :, 1] = grey_img
#proc_img[:, :, :, 2] = grey_img
proc_img = proc_img.reshape(1, 342, 257, 1)
return proc_img
# This function loads in our trained model w/ weights and compiles it
def load_model(model_path, weight_path):
# todo
json_file = open(model_path, 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
model.load_weights(weight_path)
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='rmsprop')
return model
# This function uses our device's threshold parameters to predict whether or not
# the image shows the presence of pneumonia using our trained model
def predict_image(model, img):
# todo
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='rmsprop')
#x = np.expand_dims(img, axis=0)
predict_x= model.predict(img)
print(predict_x)
prediction = np.argmax(predict_x,axis=1)
print(prediction)
return prediction
test_dicoms = ['test3.dcm','test2.dcm','test1.dcm','test4.dcm','test5.dcm','test6.dcm']
model_path = "model_adam.json" #path to saved model
weight_path = "model.h5" #path to saved best weights
#IMG_SIZE=(1,342,257,1) # This might be different if you did not use vgg16
#img_mean = 0.49262813 # mean image value from Build and train model line 22
#img_std = 0.24496286 # loads the std dev from Build and train model line 22
#my_model = load_model(model_path, weight_path) #loads model
#thresh = 0.62786263 #threshold value for New Model2 from Build and train model line 66 at 80% Precision
# use the .dcm files to test your prediction
for i in test_dicoms:
img = np.array([])
img = check_dicom(i)
if img is None:
continue
classes = {'TRAIN': ['BACTERIAL', 'NORMAL', 'VIRAL'],
'VALIDATION': ['BACTERIA', 'NORMAL'],
'TEST': ['BACTERIA', 'NORMAL', 'VIRUS']}
img_proc = preprocess_image(img)
prediction = predict_image(load_model(model_path, weight_path),img_proc)
predicted_class = classes['TRAIN'][int(prediction[0])]
print('Model Classification:', predicted_class, 'Pneumonia' )
print('--------------------------------------------------------------------------------------------------------')
Below is the output:
2022-01-02 10:50:00.817561: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2022-01-02 10:50:00.817601: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
Loading file test3.dcm ...
ID: 61 Age: 77 Modality: DX Postion: AP Body Part: CHEST Study Desc: Effusion
2022-01-02 10:50:02.652828: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory
2022-01-02 10:50:02.652859: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)
2022-01-02 10:50:02.652899: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (Wisdom-HP-250-G3-Notebook-PC): /proc/driver/nvidia/version does not exist
2022-01-02 10:50:02.653123: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
[[0.01132523 0.00254696 0.98612785]]
[2]
Model Classification: VIRAL Pneumonia
--------------------------------------------------------------------------------------------------------
Loading file test2.dcm ...
ID: 1 Age: 58 Modality: DX Postion: AP Body Part: CHEST Study Desc: Cardiomegaly
[[0.01112939 0.00251635 0.9863543 ]]
[2]
Model Classification: VIRAL Pneumonia
--------------------------------------------------------------------------------------------------------
Loading file test1.dcm ...
ID: 2 Age: 81 Modality: DX Postion: PA Body Part: CHEST Study Desc: No Finding
[[0.01128576 0.00255111 0.9861631 ]]
[2]
Model Classification: VIRAL Pneumonia
--------------------------------------------------------------------------------------------------------
Loading file test4.dcm ...
The image is not valid because the image position, the image type or the body part is not as per standards
Loading file test5.dcm ...
ID: 2 Age: 81 Modality: CT Postion: PA Body Part: CHEST Study Desc: No Finding
[[0.01128576 0.00255111 0.9861631 ]]
[2]
Model Classification: VIRAL Pneumonia
--------------------------------------------------------------------------------------------------------
Loading file test6.dcm ...
ID: 2 Age: 81 Modality: DX Postion: XX Body Part: CHEST Study Desc: No Finding
WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fba38ed19d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.
[[0.01128576 0.00255111 0.9861631 ]]
[2]
Model Classification: VIRAL Pneumonia
---------------------------------------
My suspicion is that I did it wrong on the image preprocessing steps when I have integrated these two scripts (Remember: The goal is to take advantage of the Dicom reading function of your script). Thus the model is taking in and predicting wrong input altogether due to wrong array arrangements on trying to preprocess when I have integrated these two scripts. If in need of some information on parameters in the jupyter training presentation of the viral and bacterial pneumonia model kindly highlight.
I have deliberately commented out the mean and standard deviation code on preprocessing since I wasn't clearly understanding this part and I suspect that this also might be contributing to the problem. Your help would be greatly appreciated.
ALSO WOULD APPRECIATE IF YOU ATTACH THE H5 WEIGHT FILE FOR YOUR PNEUMONIA CLASSIFIER WHICH SEEM NOT TO BE AVAILABLE IN YOU REPOSITORY.
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