To develop a deep neural network for Malaria infected cell recognition and to analyze the performance.
Using data augmentation in the Convolutional Neural Network approach decreases the chances of overfitting. Thus, Malaria detection systems using deep learning proved to be faster than most of the traditional techniques. A Convolutional Neural Network was developed and trained to classify between the parasitized and uninfected smear blood cell images. The classical image features are extracted by CNN which can extract theimage features in three different categories โ low-level, mid-level, and high-level features.
Import tensorflow and preprocessing libraries
Read the dataset
Create an ImageDataGenerator to flow image data
Build the convolutional neural network model and train the model
Fit the model
Evaluate the model with the testing data
Fit the model
Plot the performance plot
import os
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.image import imread
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import utils
from tensorflow.keras import models
from sklearn.metrics import classification_report, confusion_matrix
import tensorflow as tf
my_data_dir = '/home/ailab/hdd/dataset/cell_images'
os.listdir(my_data_dir)
test_path = my_data_dir + '/test/'
train_path = my_data_dir + '/train/'
os.listdir(train_path)
len(os.listdir(train_path + '/uninfected/'))
len(os.listdir(train_path + '/parasitized/'))
os.listdir(train_path + '/parasitized')[0]
para_img = imread(train_path + '/parasitized/' + os.listdir(train_path + '/parasitized')[0])
para_img.shape
plt.imshow(para_img)
dim1 = []
dim2 = []
for image_filename in os.listdir(test_path + '/uninfected'):
img = imread(test_path + '/uninfected' + '/' + image_filename)
d1, d2, colors = img.shape
dim1.append(d1)
dim2.append(d2)
sns.jointplot(x=dim1, y=dim2)
image_shape = (130, 130, 3)
image_gen = ImageDataGenerator(
rotation_range=20,
width_shift_range=0.10,
height_shift_range=0.10,
rescale=1/255,
shear_range=0.1,
zoom_range=0.1,
horizontal_flip=True,
fill_mode='nearest'
)
image_gen.flow_from_directory(train_path)
image_gen.flow_from_directory(test_path)
model = models.Sequential([
layers.Input((130, 130, 3)),
layers.Conv2D(32, kernel_size=3, activation="relu", padding="same"),
layers.MaxPool2D((2, 2)),
layers.Conv2D(32, kernel_size=3, activation="relu"),
layers.MaxPool2D((2, 2)),
layers.Conv2D(32, kernel_size=3, activation="relu"),
layers.MaxPool2D((2, 2)),
layers.Flatten(),
layers.Dense(32, activation="relu"),
layers.Dense(1, activation="sigmoid")
])
model.compile(loss="binary_crossentropy", metrics='accuracy', optimizer="adam")
model.summary()
train_image_gen = image_gen.flow_from_directory(train_path, target_size=image_shape[:2], color_mode='rgb',
batch_size=16, class_mode='binary')
train_image_gen.batch_size
len(train_image_gen.classes)
train_image_gen.total_batches_seen
test_image_gen = image_gen.flow_from_directory(test_path, target_size=image_shape[:2], color_mode='rgb',
batch_size=16, class_mode='binary', shuffle=False)
train_image_gen.class_indices
results = model.fit(train_image_gen, epochs=5, validation_data=test_image_gen)
model.save('cell_model1.h5')
losses = pd.DataFrame(model.history.history)
losses.plot()
model.evaluate(test_image_gen)
pred_probabilities = model.predict(test_image_gen)
predictions = pred_probabilities > 0.5
print(classification_report(test_image_gen.classes, predictions))
confusion_matrix(test_image_gen.classes, predictions)
from tensorflow.keras.preprocessing import image
img = image.load_img('new.png')
img = tf.convert_to_tensor(np.asarray(img))
img = tf.image.resize(img, (130, 130))
img = img.numpy()
type(img)
plt.imshow(img)
x_single_prediction = bool(model.predict(img.reshape(1, 130, 130, 3)) > 0.6)
print(x_single_prediction)
if x_single_prediction == 1:
print("Uninfected")
else:
print("Parasitized")
Thus, a deep neural network for Malaria infected cell recognized and analyzed the performance .