How to feed grayscale images into a pretrained neural network models? - tensorflow

Question

Noob here . I have been working with grayscale images for mitosis classification Here's a sample image I'm working with . I have employed VGGnet for achieving this. I've some doubts about how my grayscale image should be fed into the neural network. I've read the documentation of VGGnet about being trained on colored images on Imagenet.
I read the images using cv2.imread() and by squeezing into an array. I found its shape to be (227,227,3). Shouldn't it be (227,227,1) when I'm handling with grayscale images ? Model accuracy was also found to be only 50% . I'm wondering if it's something wrong with the dataset itself or VGGnet isn't suitable for this purpose. Or should I use some other method to read these images to get grayscale images?
I have tried the solutions listed in similar questions . Am I reading the images in the right way?
I'm sharing my code here.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Display Image data
from PIL import Image
import cv2
from google.colab import drive
drive.mount('/content/drive')
mitotic_image = Image.open('/content/drive/MyDrive/Medical/actualmito1/trainactmito01.jpg')
nonmitotic_image=Image.open('/content/drive/MyDrive/Medical/actualnonmito1/trainactnonmito01.jpg')
# subplotting image data
fig = plt.figure(figsize=(15,9))
ax1 = fig.add_subplot(1, 2, 1)
img_plot = plt.imshow(nonmitotic_image, cmap = plt.cm.bone)
ax1.set_title("Non-Mitotic Image")
ax2 = fig.add_subplot(1, 2, 2)
img_plot = plt.imshow(mitotic_image, cmap = plt.cm.bone)
ax2.set_title("Mitotic Image")
plt.show()
import os
yes = os.listdir("/content/drive/MyDrive/Medical/actualmito1")
no = os.listdir("/content/drive/MyDrive/Medical/actualnonmito1")
data = np.concatenate([yes, no])
target_yes = np.full(len(yes), 1)
target_no = np.full(len(no), 0)
# Image Target
data_target = np.concatenate([target_yes, target_no])
# Generate Image Data
img = cv2.imread("/content/drive/MyDrive/Medical/actualmito1/trainactmito01.jpg")
mitosis = cv2.resize(img,(32,32))
plt.imshow(mitosis)
X_data = []
yes = os.listdir("/content/drive/MyDrive/Medical/actualmito1")
for file in yes:
img = cv2.imread("/content/drive/MyDrive/Medical/actualmito1/" + file)
# resizing image data to 32x32
img = cv2.resize(img, (224,224))
X_data.append(img) # This will store list of all image data in an array
no = os.listdir("/content/drive/MyDrive/Medical/actualnonmito1")
for file in no:
img = cv2.imread("/content/drive/MyDrive/Medical/actualnonmito1/" + file)
# resizing image data to 32x32
img = cv2.resize(img, (224,224))
X_data.append(img) # This will store list of all image data in an array
X = np.squeeze(X_data)
X.shape
# Image Pixel Normalization
X = X.astype('float32')
X /= 255
X.shape
# Train & Test Data
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X, data_target, test_size = 0.1,
random_state = 3)
x_train2, x_val, y_train2, y_val = train_test_split(x_train, y_train, test_size = 0.15,
random_state = 3)
# VGG16 - Transfer Learning
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Conv2D, GlobalAveragePooling2D, Flatten, ZeroPadding2D,
Dropout, BatchNormalization
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.applications import VGG16
def build_model():
#use Imagenet = pre-trained models weights called knowledge transfer
# image_shape = 32x32x3
vgg16_model = VGG16(weights = 'imagenet', include_top = False, input_shape=(224,224,3))
# Input Layer
model = Sequential()
# paadding = 'same' = ZeroPadding
model.add(Conv2D(filters=3, kernel_size=(3,3), padding='same', input_shape = (224,224,3)))
# add transfer learning model
model.add(vgg16_model)
# Average Pooling Layer
model.add(GlobalAveragePooling2D())
model.add(BatchNormalization())
model.add(Dropout(0.5))
# Fully Connected Layer
model.add(Dense(units = 512, activation='relu'))
model.add(Dropout(0.5))
# Output Layer
model.add(Dense(units = 1, activation='sigmoid'))
model.compile(optimizer = 'Adam', loss = 'binary_crossentropy', metrics = ['Accuracy'])
return model
model = build_model()
model.summary()
from tensorflow.keras import callbacks
filepath = "/content/drive/MyDrive/BestModelMRI3.hdf5"
checkpoint = callbacks.ModelCheckpoint(filepath, monitor = 'val_loss', save_best_only = True,
mode = 'min',verbose = 1)
import datetime
import keras
import os
logdir = os.path.join("/content/drive/MyDrive/MRI_logs",
datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
tensorboard_callback = keras.callbacks.TensorBoard(logdir)
history = model.fit(x_train2, y_train2, epochs = 200, batch_size = 32, shuffle = True,
validation_data = (x_val, y_val), callbacks = [checkpoint, tensorboard_callback],verbose= 1)
model.load_weights("/content/drive/MyDrive/BestModelMRI3.hdf5")
model.evaluate(x_test, y_test)
predictions = model.predict(x_test)
yhat = np.round(predictions)
from sklearn.metrics import confusion_matrix, classification_report
confusion_matrix(y_test, yhat)
sns.heatmap(confusion_matrix(y_test, yhat), annot = True, cmap = 'RdPu')
print(classification_report(y_test, yhat))

Answer 1

VGG model was trained on RGB images. CV2 reads in images as BGR. so add code
img=cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
to convert BGR to RGB. Also VGG requires the pixels to be in the range -1 to +1 so scale with
X=X/127.5 -1