app.py

# -*- coding: utf-8 -*-
"""tumor-classification-using-keras.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1EgMc5_zGbuWuvrsGd2sg2bVfXYUQXanx

# Import Statements
"""

from zipfile import ZipFile
file_name = "brain_tumor_dataset_kaggle.zip"

with ZipFile(file_name,'r') as zip:
  zip.extractall()
  print("done")

import streamlit as st
import os
import keras 
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization
from PIL import Image
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from sklearn.preprocessing import OneHotEncoder 
encoder = OneHotEncoder()
encoder.fit([[0], [1]]) 

# 0 - Tumor
# 1 - Normal

"""
1. data list for storing image data in numpy array form
2. paths list for storing paths of all images
3. result list for storing one hot encoded form of target class whether normal or tumor"""

# This cell updates result list for images with tumor

data = []
paths = []
result = []

for r, d, f in os.walk(r'../content/brain_tumor_dataset/yes'): #r-read, d-directory, f-file
    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"../content/brain_tumor_dataset/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(-1,128,128,3)

from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(data, result, test_size=0.1, shuffle=True, random_state=0)

"""# Model Building

Batch normalization is a technique for training very deep neural networks that standardizes the inputs to a layer for each mini-batch. This has the effect of stabilizing the learning process and dramatically reducing the number of training epochs required to train deep networks.
"""

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(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())

y_train.shape

history = model.fit(x_train, y_train, epochs = 30, batch_size = 40, verbose = 1,validation_data = (x_test, y_test))

"""# Plotting Losses"""

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()

"""# Just Checking the Model"""

def names(number):
    if number==0:
        return 'Its a Tumor'
    else:
        return 'No, Its not a tumor'

from matplotlib.pyplot import imshow
img = Image.open(r"../content/brain_tumor_dataset/no/11 no.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))

from matplotlib.pyplot import imshow
img = Image.open(r"../content/brain_tumor_dataset/no/18 no.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))

"""# Thats It !!"""