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Brain_tumor_CNN

import pandas as pd import numpy as np from matplotlib import pyplot as plt %matplotlib inline import tensorflow as tf from tensorflow import keras from keras import Sequential,models,layers from tensorflow.keras.preprocessing import image from numpy import asarray import PIL from PIL import Image import os import cv2 from sklearn.model_selection import train_test_split

#Importing Datasets img_dir='../input/brain-mri-images-for-brain-tumor-detection/' no_images=os.listdir(img_dir + 'no/') yes_images=os.listdir(img_dir + 'yes/') dataset=[] lab=[]

#No tumour images for image_name in no_images: image=cv2.imread(img_dir + 'no/' +image_name) image=Image.fromarray(image,'RGB') image=image.resize((112,112)) dataset.append(np.array(image)) lab.append(0)

#Tumour images for image_name in yes_images: image=cv2.imread(img_dir + 'yes/' +image_name) image=Image.fromarray(image,'RGB') image=image.resize((112,112)) dataset.append(np.array(image)) lab.append(1)

data=np.asarray(dataset) l=np.asarray(lab) print(data.shape, l.shape)

print('Total Number of Image: ',len(l))

#Sample Images plt.imshow(data[2])

x_train,x_test,y_train,y_test = train_test_split(data,l, test_size=0.3, shuffle=True, random_state=0)

model=Sequential([

                 #cnn
                layers.Conv2D(32,(3,3),activation="relu",input_shape=(110,110,3)),
                layers.MaxPooling2D((2,2)),
 
                layers.Conv2D(64,(3,3),activation="relu"),
                layers.MaxPooling2D((2,2)),

                layers.Conv2D(128,(3,3),activation="relu"),
                layers.MaxPooling2D((2,2)),
                
                layers.Conv2D(128,(3,3),activation="relu"),
                layers.MaxPooling2D((2,2)),

                 #dense_layer
                 layers.Flatten(),
                 layers.Dense(512,activation="relu"),
                 layers.Dense(2,activation="softmax")

])

print(model.summary())

model.compile( optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"] )

model.fit(x_train, y_train, epochs = 100, batch_size = 100, verbose = 1,validation_data = (x_test, y_test))

Evaluate the model on the test data using evaluate

print("Evaluate on test data") results = model.evaluate(x_test, y_test, batch_size=16) print("test loss, test acc:", results)