To develop a deep neural network for Malaria infected cell recognition and to analyze the performance.
Malaria dataset of 27,558 cell images with an equal number of parasitized and uninfected cells. A level-set based algorithm was applied to detect and segment the red blood cells. The images were collected and annotated by medical professionals.Here we build a convolutional neural network model that is able to classify the cells.
Import tensorflow and preprocessing libraries
Download and load the dataset folder
Split the training and testing folders.
Perform image data generation methods.
Build the convolutional neural network model
Train the model with the training data
Plot the performance plot
Evaluate the model with the testing data using probability prediction(uninfected-> prob>0.5,parasitized-> <=0.5)
Fit the model and predict the sample input.
# Developed By:P.Suganya
# Register Number:212220230049
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
# to share the GPU resources for multiple sessions
from tensorflow.compat.v1.keras.backend import set_session
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True # dynamically grow the memory used on the GPU
config.log_device_placement = True # to log device placement (on which device the operation ran)
sess = tf.compat.v1.Session(config=config)
set_session(sess)
%matplotlib inline
# for college server
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)
# Checking the image dimensions
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, # rotate the image 20 degrees
width_shift_range=0.10, # Shift the pic width by a max of 5%
height_shift_range=0.10, # Shift the pic height by a max of 5%
rescale=1/255, # Rescale the image by normalzing it.
shear_range=0.1, # Shear means cutting away part of the image (max 10%)
zoom_range=0.1, # Zoom in by 10% max
horizontal_flip=True, # Allo horizontal flipping
fill_mode='nearest' # Fill in missing pixels with the nearest filled value
)
image_gen.flow_from_directory(train_path)
image_gen.flow_from_directory(test_path)
model = models.Sequential()
model.add(layers.Input(shape=(130,130,3)))
model.add(layers.Conv2D(filters=32,kernel_size=(3,3),padding="same",activation='relu'))
model.add(layers.AvgPool2D(pool_size=(2,2)))
model.add(layers.Conv2D(filters=32,kernel_size=(3,3),padding="same",activation='relu'))
model.add(layers.MaxPool2D(pool_size=(2,2)))
model.add(layers.Flatten())
model.add(layers.Dense(32,activation='relu'))
model.add(layers.Dense(1, activation ='sigmoid'))
model.summary()
model.compile(optimizer='Adam',
loss='binary_crossentropy',
metrics=['accuracy'])
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=batch_size,
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_model.h5')
losses = pd.DataFrame(model.history.history)
losses.plot()
model.evaluate(test_image_gen)
pred_probabilities = model.predict(test_image_gen)
test_image_gen.classe
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('mui.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("Cell is UNINFECTED")
else:
print("Cell is PARASITIZED")
Thus, a deep neural network for Malaria infected cell recognition is developed and the performance is analyzed.