Source code

!pip install opendatasets
import opendatasets as od
dataset1='https://www.kaggle.com/datasets/jakeshbohaju/brain-tumor'
dataset2='https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection'
od.download(dataset1)
od.download(dataset2)
import os
data_dir1='.\\brain-tumor' 
data_dir2='.\\brain-mri-images-for-brain-tumor-detection'
os.listdir(data_dir1)
os.listdir(data_dir2)
import os
from os import listdir

paths=[]

for dirname, _, filenames in os.walk('.\\brain-mri-images-for-brain-tumor-detection'):
    for filename in filenames:
        paths.append(os.path.join(dirname, filename))
        print(os.path.join(dirname, filename))

!pip install imutils
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.layers import Conv2D, Input, ZeroPadding2D, BatchNormalization, Activation, Dropout, MaxPooling2D, Flatten, Dense
from tensorflow.keras.models import Model, load_model, Sequential
from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint

from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score
from sklearn.utils import shuffle

import matplotlib.pyplot as plt
import time
import imutils

import cv2      #open cv


from PIL import Image 
from sklearn.preprocessing import OneHotEncoder 
def contour(image , plot = False):
    grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)                         #grayscaling the images
    grayscale = cv2.GaussianBlur(grayscale, (5,5),0)                            #blur the image to bring it under the threshold
    threshold_image = cv2.threshold(grayscale, 50, 255, cv2.THRESH_BINARY)[1]   #convert these grayscaled images to binary images
    threshold_image = cv2.erode(threshold_image, None, iterations=2)            #to remove the regions of noise
    threshold_image = cv2.dilate(threshold_image, None, iterations=2)           #to remove all the noises around the image
    
    #Now we need to find the contour and clean it to get what is inside the image.
    contour = cv2.findContours(threshold_image.copy(),cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) 
    
     #we grab the largest contour using the max.
    contour = imutils.grab_contours(contour)
    c = max(contour, key=cv2.contourArea)
    
    #Now we limit the image by finding it's extreme points.
    ext_left = tuple(c[c[:,:,0].argmin()][0])
    ext_right = tuple(c[c[:,:,0].argmax()][0])
    ext_top = tuple(c[c[:,:,1].argmin()][0])
    ext_bot = tuple(c[c[:,:,1].argmax()][0])
    
    processed_image = image[ext_top[1]:ext_bot[1],ext_left[0]:ext_right[0]]
        
    if plot:
        plt.figure()
        plt.subplot(1,2,1)
        plt.imshow(image)
        
        plt.tick_params(axis="both", which="both",
                       top= False, bottom= False,left= False,right= False,
                        labeltop= False, labelbottom= False,
                        labelleft= False,labelright= False)
        plt.title("ORIGINAL")
        
        plt.subplot(1,2,2)
        plt.imshow(processed_image)
        
        plt.tick_params(axis="both", which="both",
                       top= False, bottom= False,left= False,right= False,
                        labeltop= False, labelbottom= False,
                        labelleft= False,labelright= False)
        plt.title("PROCESSED")
        plt.show()
        
    return processed_image
for path in paths:
    img = Image.open(path)
    img = img.resize((128,128))
    #img.save()
    break
Image.open(paths[0])
for path in paths:
    img = cv2.imread(path)
    img = contour(img, True)
    """plt.imsave(img,path)"""
encoder = OneHotEncoder()
encoder.fit([[0], [1]])
# This cell updates result list for images with tumor
data = []
paths = []
result = []

for r, d, f in os.walk(r'.\yes'):
    for file in f:
        if '.jpg' in file:
            paths.append(os.path.join(r, file))

for path in paths:
    img = Image.open(path)
    img = img.resize((128,128))
    img = np.array(img)
    if(img.shape == (128,128,3)):
        data.append(np.array(img))
        result.append(encoder.transform([[0]]).toarray())
# This cell updates result list for images without tumor

paths = []
for r, d, f in os.walk(r".\no"):
    for file in f:
        if '.jpg' in file:
            paths.append(os.path.join(r, file))

for path in paths:
    img = Image.open(path)
    img = img.resize((128,128))
    img = np.array(img)
    if(img.shape == (128,128,3)):
        data.append(np.array(img))
        result.append(encoder.transform([[1]]).toarray())
data = np.array(data)
data.shape
result = np.array(result)
result = result.reshape(139,2)
x_train,x_test,y_train,y_test = train_test_split(data, result, test_size=0.2, shuffle=True, random_state=0)
model = Sequential()

model.add(Conv2D(32, kernel_size=(2, 2), input_shape=(128, 128, 3), padding = 'Same'))
model.add(Conv2D(32, kernel_size=(2, 2),  activation ='relu', padding = 'Same'))


model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(64, kernel_size = (2,2), activation ='relu', padding = 'Same'))
model.add(Conv2D(64, kernel_size = (2,2), activation ='relu', padding = 'Same'))

model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
model.add(Dropout(0.25))

model.add(Flatten())

model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))

model.compile(loss = "categorical_crossentropy", optimizer='Adamax')
print(model.summary())

history = model.fit(x_train, y_train, epochs = 30, batch_size = 40, verbose = 1,validation_data = (x_test, y_test))
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Test', 'Validation'], loc='upper right')
plt.show()
def names(cat):
    if cat==0:
        return 'Tumor detected'
    else:
        return 'No detected tumor'
from matplotlib.pyplot import imshow
img = Image.open(r"./yes/Y69.jpg")
x = np.array(img.resize((128,128)))
x = x.reshape(1,128,128,3)
res = model.predict_on_batch(x)
classification = np.where(res == np.amax(res))[1][0]
imshow(img)
print(str(res[0][classification]*100) + '% Confidence This Is ' + names(classification))
from matplotlib.pyplot import imshow
img = Image.open(r"./no/N5.jpg")
x = np.array(img.resize((128,128)))
x = x.reshape(1,128,128,3)
res = model.predict_on_batch(x)
classification = np.where(res == np.amax(res))[1][0]
imshow(img)
print(str(res[0][classification]*100) + '% Confidence This Is A ' + names(classification))