mainTain.py

import cv2
import os
import tensorflow as tf
from tensorflow import keras
from PIL import Image
import numpy as np
from sklearn.model_selection import train_test_split
from keras.utils import normalize
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.utils import to_categorical

img_dir='datasets/'

notum=os.listdir(img_dir+ 'no/')
yestum=os.listdir(img_dir+ 'yes/')
dataset=[]
label=[]

inpsize=64
#print(notum)
#path='no0.jpg'
#print(path.split('.')[1])

for i , image_name in enumerate(notum):
    if(image_name.split('.')[1]=='jpg'):
        image=cv2.imread(img_dir+'no/'+image_name)
        image=Image.fromarray(image, 'RGB')
        image=image.resize((inpsize,inpsize))
        dataset.append(np.array(image))
        label.append(0)

for i , image_name in enumerate(yestum):
    if(image_name.split('.')[1]=='jpg'):
        image=cv2.imread(img_dir+'yes/'+image_name)
        image=Image.fromarray(image, 'RGB')
        image=image.resize((inpsize,inpsize))
        dataset.append(np.array(image))
        label.append(1)

dataset=np.array(dataset)
label=np.array(label)

x_train, x_test, y_train, y_test=train_test_split(dataset, label, test_size=0.2, random_state=0)
#print(x_train.shape)
#print(y_train.shape)

#print(x_test.shape)
#print(y_test.shape)

x_train=normalize(x_train, axis=1)
x_test=normalize(x_test, axis=1)

y_train=to_categorical(y_train , num_classes=2)
y_test=to_categorical(y_test, num_classes=2)

#Building the model

model=Sequential()

model.add(Conv2D(32, (3,3), input_shape=(inpsize, inpsize, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(32, (3,3), kernel_initializer='he_uniform'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(64, (3,3), kernel_initializer='he_uniform'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(2))
model.add(Activation('softmax'))

# Binary CrossEntropy=1, sigmoid
# Categorical Cross Entropy=2, softmax

model.compile(loss='categorical_crossentropy',optimizer='adam', metrics=['accuracy'])

model.fit(x_train, y_train, batch_size=16, verbose=1, epochs=10, validation_data=(x_test, y_test), shuffle=False)

model.save('BrainTumor10EpochCategorical.h5')