MRI_Image_processing_v1..ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MRI Classification\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras import models,datasets,layers\n",
    "import tensorflow as tf\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split\n",
    "import imutils\n",
    "import os\n",
    "from os import listdir\n",
    "import SimpleITK\n",
    "from imutils import paths\n",
    "    ##image = cv.imread(directory + \"\\ \" + filename)\n",
    "import cv2 as cv\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## function to crop the image to only the important areas\n",
    "def crop_brain_contour(org_img):\n",
    "    #converting to gray scale\n",
    "    ##un-comment this the see the RGB and Gray scale image\n",
    "    ## plt.subplot(1,2,1)\n",
    "    ##plt.inshow(org_img)\n",
    "    gray = cv.cvtColor(org_img,cv.COLOR_BGR2GRAY)\n",
    "    \n",
    "    ##Smoothing the imgage \n",
    "    ##bilateral filtering\n",
    "    blur1 = cv.bilateralFilter(gray,9,75,75)\n",
    "    #gaussian filter\n",
    "    blur2 = cv.GaussianBlur(gray,(5,5),0)\n",
    "    # Threshold the image, then perform a series of erosions +\n",
    "    # dilations to remove any small regions of noise\n",
    "    thresh = cv.threshold(blur1, 45, 255, cv.THRESH_BINARY)[1]\n",
    "    thresh = cv.erode(thresh, None, iterations=2)\n",
    "    thresh = cv.dilate(thresh, None, iterations=2)\n",
    "\n",
    "    # Find contours in thresholded image, then grab the largest one\n",
    "    cnts = cv.findContours(thresh.copy(), cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)\n",
    "    cnts = imutils.grab_contours(cnts)\n",
    "    c = max(cnts, key=cv.contourArea)\n",
    "    \n",
    "    # Find the extreme points\n",
    "    extLeft = tuple(c[c[:, :, 0].argmin()][0])\n",
    "    extRight = tuple(c[c[:, :, 0].argmax()][0])\n",
    "    extTop = tuple(c[c[:, :, 1].argmin()][0])\n",
    "    extBot = tuple(c[c[:, :, 1].argmax()][0])\n",
    "\n",
    "    # crop new image out of the original image using the four extreme points (left, right, top, bottom)\n",
    "    new_image = org_img[extTop[1]:extBot[1], extLeft[0]:extRight[0]]        \n",
    "    return new_image\n",
    "## here we have a CROPPED image with only the important part of the scane\n",
    "## we have used bi_lateral and gaussian filters on the image as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=[]     ## Images    \n",
    "Y=[]     ## Labels \n",
    "images_yes = list(paths.list_images('/Users/apple/Desktop/Image processing/archive/yes'))\n",
    "for i in images_yes:\n",
    "    # load the image\n",
    "        image = cv.imread(i)\n",
    "        # crop the brain and ignore the unnecessary rest part of the image\n",
    "        #####image = crop_brain_contour(image)\n",
    "        # resize image\n",
    "        image = cv.resize(image, dsize=(240, 240), interpolation=cv.INTER_CUBIC)\n",
    "        # normalize values\n",
    "        image = image / 255.\n",
    "        # convert image to numpy array and append it to X\n",
    "        X.append(image)\n",
    "        Y.append(1)\n",
    "        ########## We have here X --> images of tumour vala brain Y--> all 1's\n",
    "images_no = list(paths.list_images('/Users/apple/Desktop/Image processing/archive/no'))\n",
    "for i in images_no:\n",
    "    # load the image\n",
    "        image = cv.imread(i)\n",
    "        # crop the brain and ignore the unnecessary rest part of the image\n",
    "        image = crop_brain_contour(image)\n",
    "        # resize image\n",
    "        image = cv.resize(image, dsize=(240, 240), interpolation=cv.INTER_CUBIC)\n",
    "        # normalize values\n",
    "        image = image / 255.\n",
    "        # convert image to numpy array and append it to X\n",
    "        X.append(image)\n",
    "        Y.append(0)\n",
    "X = np.array(X)\n",
    "Y = np.array(Y)\n",
    "    \n",
    "    # Shuffle the data\n",
    "X, Y = shuffle(X, Y)    \n",
    "\n",
    "## printing an image\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Croppping and Filtering "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(240, 240, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## using the below crop_brain_contour###\n",
    "#for image in X:\n",
    " #   image = crop_brain_contour(image)\n",
    "X[1].shape   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(169, 240, 240, 3)\n",
      "(169,)\n"
     ]
    }
   ],
   "source": [
    "## Data loading\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=42)\n",
    "## Data normalising\n",
    "X_train,X_test = X_train/255,X_test/255\n",
    "print(X_train.shape)\n",
    "print(Y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Neural Network \n",
    "model = models.Sequential()\n",
    "## 1st Layer is convolutional layer with relu activation function\n",
    "model.add(layers.Conv2D(32,(3,3),activation ='relu',input_shape=(240,240,3)))\n",
    "## 2nd Layer is Maxpooling layer for the convolutional layer\n",
    "model.add(layers.MaxPooling2D((2,2)))\n",
    "## 3rd and 4th layer are same as above. 4th layer has 32 units\n",
    "model.add(layers.Conv2D(64,(3,3),activation = 'relu' ))\n",
    "model.add(layers.MaxPooling2D((2,2)))\n",
    "## 5th layer is another convolutional layer with 32 units\n",
    "model.add(layers.Conv2D(64,(3,3),activation = 'relu' ))\n",
    "##6th layer is Flattening layee\n",
    "model.add(layers.Flatten())\n",
    "##7th layer is a dense layer with 64 units with relu activation function\n",
    "model.add(layers.Dense(units= 64,activation='relu'))\n",
    "##7th layer is a dense layer with 64 units with relu activation function\n",
    "model.add(layers.Dense(units= 32,activation='relu'))\n",
    "## 8th and the final layer is another dense layer with softmax function with 2 units\n",
    "model.add(layers.Dense(units= 2,activation='sigmoid'))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "6/6 [==============================] - 10s 2s/step - loss: 0.6763 - accuracy: 0.6331 - val_loss: 0.7103 - val_accuracy: 0.5714\n",
      "Epoch 2/10\n",
      "6/6 [==============================] - 9s 2s/step - loss: 0.6684 - accuracy: 0.6331 - val_loss: 0.6854 - val_accuracy: 0.5714\n",
      "Epoch 3/10\n",
      "6/6 [==============================] - 9s 1s/step - loss: 0.6632 - accuracy: 0.6331 - val_loss: 0.6818 - val_accuracy: 0.5714\n",
      "Epoch 4/10\n",
      "6/6 [==============================] - 9s 1s/step - loss: 0.6611 - accuracy: 0.6331 - val_loss: 0.6888 - val_accuracy: 0.5714\n",
      "Epoch 5/10\n",
      "6/6 [==============================] - 9s 1s/step - loss: 0.6587 - accuracy: 0.6331 - val_loss: 0.6985 - val_accuracy: 0.5714\n",
      "Epoch 6/10\n",
      "6/6 [==============================] - 9s 1s/step - loss: 0.6589 - accuracy: 0.6331 - val_loss: 0.6852 - val_accuracy: 0.5714\n",
      "Epoch 7/10\n",
      "6/6 [==============================] - 9s 1s/step - loss: 0.6556 - accuracy: 0.6331 - val_loss: 0.6899 - val_accuracy: 0.5714\n",
      "Epoch 8/10\n",
      "6/6 [==============================] - 9s 1s/step - loss: 0.6547 - accuracy: 0.6331 - val_loss: 0.6914 - val_accuracy: 0.5714\n",
      "Epoch 9/10\n",
      "6/6 [==============================] - 9s 1s/step - loss: 0.6502 - accuracy: 0.6331 - val_loss: 0.6726 - val_accuracy: 0.5714\n",
      "Epoch 10/10\n",
      "6/6 [==============================] - 9s 1s/step - loss: 0.6424 - accuracy: 0.6331 - val_loss: 0.6519 - val_accuracy: 0.5714\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x14e44a250>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'],)\n",
    "model.fit(X_train,Y_train,epochs=10,validation_data=(X_test,Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 1s 252ms/step - loss: 0.6519 - accuracy: 0.5714\n",
      "0.651946485042572 is the loss\n",
      "0.5714285969734192 is the accuracy\n",
      "WARNING:tensorflow:From /opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
      "WARNING:tensorflow:From /opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
      "INFO:tensorflow:Assets written to: MRI_image_class_V1.model/assets\n"
     ]
    }
   ],
   "source": [
    "## Loss and Accuracy of the model\n",
    "loss,accuracy = model.evaluate(X_test,Y_test)\n",
    "\n",
    "print(str(loss) + ' is the loss')\n",
    "print(str(accuracy) + ' is the accuracy')\n",
    "\n",
    "model.save('MRI_image_class_V1.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yes,tumour is present\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "result =['no,tumour is not present','yes,tumour is present']\n",
    "test_image = cv.imread('/Users/apple/Desktop/Test_3.jpg')\n",
    "plt.imshow(test_image)\n",
    "test_image = cv.resize(test_image, dsize=(240, 240), interpolation=cv.INTER_CUBIC)\n",
    "        # normalize values\n",
    "test_image = test_image / 255\n",
    "prediction = model.predict(np.array([test_image])/255)\n",
    "index = np.argmax(prediction)\n",
    "print(result[index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yes,tumour is present\n",
      "yes,tumour is present\n",
      "yes,tumour is present\n",
      "yes,tumour is present\n",
      "yes,tumour is present\n",
      "yes,tumour is present\n",
      "yes,tumour is present\n",
      "yes,tumour is present\n"
     ]
    }
   ],
   "source": [
    "for img in X_test:\n",
    "    result =['no,tumour is not present','yes,tumour is present']\n",
    "    test_image = img\n",
    "    test_image = cv.resize(test_image, dsize=(240, 240), interpolation=cv.INTER_CUBIC) # normalize values\n",
    "    test_image = test_image / 255\n",
    "    prediction = model.predict(np.array([test_image])/255)\n",
    "    index = np.argmax(prediction)\n",
    "    if result[index] == 'yes,tumour is present':\n",
    "        plt.imshow(test_image)\n",
    "        print(result[index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}