COV19-CT-DB-CNN-model.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 23 21:29:01 2021

@author: idu
"""

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Sep  7 00:28:03 2021

@author: idu
"""

import os, glob
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow import keras
from keras import backend as K
from tensorflow.keras.optimizers import Adam
from keras import layers, models
from tensorflow.keras import activations

####### Generatiiing Data

#batch_size = 32
#batch_size = 64
batch_size = 128

SIZE = 512

train_datagen = ImageDataGenerator(rescale=1./255)
#Change the training path '/home/idu/Desktop/COV19D/train/' to where your training set's directory is                             
train_generator = train_datagen.flow_from_directory(
        '/home/idu/Desktop/COV19D/train/',  ## COV19-CT-DB Training set (335672 Images)
        target_size=(SIZE, SIZE),
        batch_size=batch_size,
        color_mode='grayscale',
        classes = ['covid','non-covid'],
        class_mode='binary')

val_datagen = ImageDataGenerator(rescale=1./255)
# Change the training path '/home/idu/Desktop/COV19D/validation/' to where your validaiton set's directory is   
val_generator = val_datagen.flow_from_directory(
        '/home/idu/Desktop/COV19D/validation/',  ## COV19-CT-DB Validation set (75532 images)
        target_size=(SIZE, SIZE),
        batch_size=batch_size,
        color_mode='grayscale',
        classes = ['covid','non-covid'],
        class_mode='binary')

################ CNN Model Architecture
def make_model():
   
    model = models.Sequential()
    
    # Convulotional Layer 1
    model.add(layers.Conv2D(16,(3,3),input_shape=(SIZE,SIZE,1), padding="same"))
    model.add(layers.BatchNormalization())
    model.add(layers.ReLU())
    model.add(layers.MaxPooling2D((2,2)))
    
    # Convulotional Layer 2
    model.add(layers.Conv2D(32,(3,3), padding="same"))  
    model.add(layers.BatchNormalization())
    model.add(layers.ReLU())
    model.add(layers.MaxPooling2D((2,2)))
    
    # Convulotional Layer 3
    model.add(layers.Conv2D(64,(3,3), padding="same"))
    model.add(layers.BatchNormalization())
    model.add(layers.ReLU())   
    model.add(layers.MaxPooling2D((2,2)))
    
    # Convulotional Layer 4
    model.add(layers.Conv2D(128,(3,3), padding="same"))
    model.add(layers.BatchNormalization())
    model.add(layers.ReLU())
    model.add(layers.MaxPooling2D((2,2)))
    
    # Fully Connected Layer
    model.add(layers.Flatten())
    model.add(layers.Dense(256))
    model.add(layers.BatchNormalization())
    model.add(layers.ReLU())
    model.add(layers.Dropout(0.1))
    
    # Dense Layer  
    model.add(layers.Dense(1,activation='sigmoid'))
    
    
    return model


model = make_model()

###################################### Compiling and Training the model
n_epochs= 70

model.compile(loss='binary_crossentropy',
             optimizer='adam',
             metrics=[tf.keras.metrics.Precision(),tf.keras.metrics.Recall(),'accuracy'])

history=model.fit(train_generator, 
                  steps_per_epoch=250,
                  validation_data=val_generator,
                  validation_steps=78,
                  verbose=2,
                  epochs=n_epochs)

######################## Evaluating
print (history.history.keys())
            
Train_accuracy = history.history['accuracy']
print(Train_accuracy)
print(np.mean(Train_accuracy))
val_accuracy = history.history['val_accuracy']
print(val_accuracy)
print( np.mean(val_accuracy))

epochs = range(1, len(Train_accuracy)+1)
plt.figure(figsize=(12,6))
plt.plot(epochs, Train_accuracy, 'g', label='Training acc')
plt.plot(epochs, val_accuracy, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('accuracy')
plt.ylim(0.45,1)
plt.xlim(0,50)
plt.legend()

plt.show()

val_recall = history.history['val_recall']
print(val_recall)
avg_recall = np.mean(val_recall)
avg_recall

val_precision = history.history['val_precision']
avg_precision = np.mean(val_precision)
avg_precision

epochs = range(1, len(Train_accuracy)+1)
plt.figure(figsize=(12,6))
plt.plot(epochs, val_recall, 'g', label='Validation Recall')
plt.plot(epochs, val_precision, 'b', label='Validation Prcision')
plt.title('Validation recall and Validation Percision')
plt.xlabel('Epochs')
plt.ylabel('Recall and Precision')
plt.legend()
plt.ylim(0,1)

plt.show()

Macro_F1score = (2*avg_precision*avg_recall)/ (avg_precision + avg_recall)
Macro_F1score