Malarial-Cell-Classification/Maleria_Cell_Classification.py at main · devanshpratapsingh/Malarial-Cell-Classification · GitHub

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import numpy as np
np.random.seed(1000)

import matplotlib.pyplot as plt
import cv2
import os
from PIL import Image
import keras

os.environ['KERAS_BACKEND'] = 'tensorflow'

image_directory = 'cell_images/'
SIZE = 64
dataset = []
label = []

parasitized_images = os.listdir(image_directory + 'Parasitized/')
for i, image_name in enumerate(parasitized_images):
    if (image_name.split('.')[1] == 'png'):
        image = cv2.imread(image_directory + 'Parasitized/' + image_name)
        image = Image.fromarray(image, 'RGB')
        image = image.resize((SIZE, SIZE))
        dataset.append(np.array(image))
        label.append(0)

uninfected_images = os.listdir(image_directory + 'Uninfected/')
for i, image_name in enumerate(uninfected_images):
    if (image_name.split('.')[1] == 'png'):
        image = cv2.imread(image_directory + 'Uninfected/' + image_name)
        image = Image.fromarray(image, 'RGB')
        image = image.resize((SIZE, SIZE))
        dataset.append(np.array(image))
        label.append(1)


INPUT_SHAPE = (SIZE, SIZE, 3)
inp = keras.layers.Input(shape=INPUT_SHAPE)

conv1 = keras.layers.Conv2D(32, kernel_size=(3,3), activation='relu', padding='same')(inp)
pool1 = keras.layers.MaxPool2D(pool_size=(2,2))(conv1)
norm1 = keras.layers.BatchNormalization(axis=-1)(pool1)
drop1 = keras.layers.Dropout(rate=0.2)(norm1)

conv2 = keras.layers.Conv2D(32, kernel_size=(3,3), activation='relu', padding='same')(drop1)
pool2 = keras.layers.MaxPool2D(pool_size=(2,2))(conv2)
norm2 = keras.layers.BatchNormalization(axis=-1)(pool2)
drop2 = keras.layers.Dropout(rate=0.2)(norm2)

flat = keras.layers.Flatten()(drop2)

hidden1 = keras.layers.Dense(521, activation='relu')(flat)
norm3 = keras.layers.BatchNormalization(axis=-1)(hidden1)
drop3 = keras.layers.Dropout(rate=0.2)(norm3)

hidden2 = keras.layers.Dense(521, activation='relu')(drop3)
norm4 = keras.layers.BatchNormalization(axis=-1)(hidden2)
drop4 = keras.layers.Dropout(rate=0.2)(norm4)

hidden3 = keras.layers.Dense(521, activation='relu')(flat)
norm5 = keras.layers.BatchNormalization(axis=-1)(hidden3)
drop5 = keras.layers.Dropout(rate=0.2)(norm5)

out = keras.layers.Dense(2, activation='sigmoid')(drop5)

model = keras.Model(inputs=inp, outputs=out)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

print(model.summary())

###############

from sklearn.model_selection import train_test_split
from keras.utils import to_categorical

X_train, X_test, y_train, y_test = train_test_split(dataset, to_categorical(np.array(label)), test_size = 0.20, random_state = 0)

history = model.fit(np.array(X_train),
                         y_train,
                         batch_size = 64,
                         verbose = 1,
                         epochs = 10,      #Changed to 3 from 50 for testing purposes.
                         validation_split = 0.1,
                         shuffle = False
                      #   callbacks=callbacks
                     )


print("Test_Accuracy: {:.2f}%".format(model.evaluate(np.array(X_test), np.array(y_test))[1]*100))


f, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
t = f.suptitle('CNN Performance', fontsize=12)
f.subplots_adjust(top=0.85, wspace=0.3)

max_epoch = len(history.history['accuracy'])+1
epoch_list = list(range(1,max_epoch))
ax1.plot(epoch_list, history.history['accuracy'], label='Train Accuracy')
ax1.plot(epoch_list, history.history['val_accuracy'], label='Validation Accuracy')
ax1.set_xticks(np.arange(1, max_epoch, 5))
ax1.set_ylabel('Accuracy Value')
ax1.set_xlabel('Epoch')
ax1.set_title('Accuracy')
l1 = ax1.legend(loc="best")

ax2.plot(epoch_list, history.history['loss'], label='Train Loss')
ax2.plot(epoch_list, history.history['val_loss'], label='Validation Loss')
ax2.set_xticks(np.arange(1, max_epoch, 5))
ax2.set_ylabel('Loss Value')
ax2.set_xlabel('Epoch')
ax2.set_title('Loss')
l2 = ax2.legend(loc="best")


#Save the model
model.save('malaria_cnn.h5')

#Devansh Pratap Singh