Upload app.py

app.py

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+# -*- 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 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(139,2)
+
+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 !!"""