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Manojvibranium/
MalariaCellDetection

Name: MalariaCellDetection
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Deep Neural Network for Malaria Infected Cell Recognition

AIM

To develop a deep neural network for Malaria infected cell recognition and to analyze the performance.

Problem Statement and Dataset

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 – low-level, mid-level, and high-level features.

Neural Network Model

DESIGN STEPS

STEP 1:

Import tensorflow and preprocessing libraries

STEP 2:

Read the dataset

STEP 3:

Create an ImageDataGenerator to flow image data

STEP 4:

Build the convolutional neural network model and train the model

STEP 5:

Fit the model

STEP 6:

Evaluate the model with the testing data

STEP 7:

Fit the model

STEP 8:

Plot the performance plot

PROGRAM

Developed BY : Manoj Choudhary V

Reg no:212221240025

# Importing Modules

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

DataDirectory

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]

Import and display an image

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,
    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)

Create a neural network model

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()

Data generation for training and testing

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

Train the model

results = model.fit(train_image_gen, epochs=5, validation_data=test_image_gen)
model.save('cell_model1.h5')

Visualize the training losses

losses = pd.DataFrame(model.history.history)
losses.plot()

Evaluate the model

model.evaluate(test_image_gen)

Make predictions and evaluate the model

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)

Load and process a single image for prediction

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")

OUTPUT

Training Loss, Validation Loss Vs Iteration Plot

Classification Report

Confusion Matrix

Single Data prediction

RESULT

Thus, a deep neural network for Malaria infected cell recognized and analyzed the performance .

Manojvibranium/MalariaCellDetection