braintumor.py

# -*- coding: utf-8 -*-
"""BrainTumor.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1DWAMCDSmhd_JtBIkThzNWXGxiJTOLWHW
"""

from google.colab import files
files.upload()

!pip install -q kaggle

!mkdir -p ~/.kaggle
!cp kaggle.json ~/.kaggle/

!chmod 600 /root/.kaggle/kaggle.json
!kaggle datasets download -d navoneel/brain-mri-images-for-brain-tumor-detection

import keras
from keras.models import *
from keras.layers import *
from keras.preprocessing import image
import PIL

import pandas as pd
import matplotlib.pyplot as plt

!unzip "/content/brain-mri-images-for-brain-tumor-detection.zip"

import os
 
# assign directory
directory = '/content/no'
no=[]
# iterate over files in
# that directory
for filename in os.scandir(directory):
    if filename.is_file():
        no.append(filename.path)

len(no)

import os
 
# assign directory
directory = '/content/yes'
yes=[]
# iterate over files in
# that directory
for filename in os.scandir(directory):
    if filename.is_file():
        yes.append(filename.path)

len(yes)

x_train = no[:80] + yes[:130]

x_test = no[80:] + yes[130:]

y_train = []

for i in range(80):
  y_train.append(0)
for i in range(130):
  y_train.append(1)

len(y_train)

y_test =[] 

for i in range(18):
  y_test.append(0)
for i in range(25):
  y_test.append(1)

len(y_test)

# Import the necessary libraries
import cv2
from numpy import asarray
import numpy as np
# load the image and convert into 
# numpy array
x_train_final =[]
for i in x_train:
       img =cv2.imread(i,0)
       img=cv2.resize(img, (100, 100)) 
       x_train_final.append(np.array(img).astype(np.uint8))
   
# data
print(x_train_final)

# Import the necessary libraries
import cv2
from numpy import asarray
import numpy as np
# load the image and convert into 
# numpy array
x_test_final =[]
for i in x_test:
       img =cv2.imread(i,0)
       img=cv2.resize(img, (100, 100)) 
       x_test_final.append(np.array(img).astype(np.uint8))
   
# data
print(x_test_final)

print(len(x_train_final))
print(len(x_test_final))

import numpy as np 
x_train_final=np.array(x_train_final)
x_test_final=np.array(x_test_final)
y_train=np.array(y_train)
y_test=np.array(y_test)

print(x_train_final.shape)
print(x_test_final.shape)

# normlize the data
x_train = x_train_final/255.0
x_test  = x_test_final/255.0

model=keras.Sequential() #Create a network sequence.
model.add(Input(shape=(100,100,1)))
model.add(Conv2D(filters=6,kernel_size = 3,strides = (2,2), padding = 'same',activation = 'relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides = (2,2), padding = 'valid'))

model.add(Conv2D(filters=6,kernel_size = 3,strides = (2,2), padding = 'same',activation = 'relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides = (2,2), padding = 'valid'))

model.add(Conv2D(filters=6,kernel_size = 3,strides = (2,2),padding = 'same',activation = 'relu'))
model.add(Conv2D(filters=7,kernel_size = 3,strides = (2,2),padding = 'same',activation = 'relu'))


model.add(Flatten())
model.add(Dense(84,activation = 'relu'))
model.add(Dense(70,activation = 'relu'))
model.add(Dense(10,activation = 'relu'))
model.add(Dense(1,activation="sigmoid"))

model.summary()

model.compile(optimizer='adam',loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),metrics=['accuracy'])

results= model.fit(x_train,y_train,epochs=100,batch_size=30,validation_data=(x_test, y_test),shuffle=True)

model.save("BrainTumor_CNN.h5")
from google.colab import drive
drive.mount('/content/drive')
from keras.models import load_model
!cp "/content/BrainTumor_CNN.h5" "/content/drive/MyDrive/H5_Folders"
model=load_model('/content/BrainTumor_CNN.h5')

y_pred=model.predict(x_test)

import matplotlib.pyplot as plt
plt.plot(results.history['loss'])
plt.plot(results.history['val_loss'])
plt.legend(['Training','Vaildation'])
plt.title('Training and Validation Losses')
plt.xlabel('epoches')
plt.ylabel('losses')

import matplotlib.pyplot as plt
plt.plot(results.history['accuracy'])
plt.plot(results.history['val_accuracy'])
plt.legend(['Training','Vaildation'])
plt.title('Training and Validation Accuracy')
plt.xlabel('epoches')
plt.ylabel('accuracy')

print(model.evaluate(x_test,y_test))