120. Image Classfication with naive_bayes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "-WASZeWsCOaX",
    "outputId": "c07ff435-3a0e-438d-a0e3-e94076e30bf1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Collecting segmentation-models\n",
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      "Installing collected packages: keras-applications, image-classifiers, efficientnet, segmentation-models\n",
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      "Installing collected packages: gdown\n",
      "  Attempting uninstall: gdown\n",
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      "/usr/local/lib/python3.8/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
      "  warnings.warn(\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1-Vm4Y4YTPSH_zM3xSTzB9RQvJqiVFwaw\n",
      "To: /content/Tumor.zip\n",
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      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Collecting patool\n",
      "  Downloading patool-1.12-py2.py3-none-any.whl (77 kB)\n",
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      "\u001b[?25hInstalling collected packages: patool\n",
      "Successfully installed patool-1.12\n",
      "patool: Extracting Tumor.zip ...\n",
      "patool: running /usr/bin/7z x -o./Unpack_i3bor71w -- Tumor.zip\n",
      "patool: ... Tumor.zip extracted to `Tumor'.\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'Tumor'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!pip install segmentation-models\n",
    "!pip install tensorflow\n",
    "!pip install --upgrade --no-cache-dir gdown\n",
    "!gdown --id 1-Vm4Y4YTPSH_zM3xSTzB9RQvJqiVFwaw\n",
    "!pip install patool\n",
    "import patoolib\n",
    "patoolib.extract_archive('Tumor.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7B_8GDTgCPSo",
    "outputId": "dcdd1704-d89f-4b74-e996-0390968f6010"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pituitary\n",
      "meningioma\n",
      "glioma\n",
      "notumor\n",
      "pituitary\n",
      "meningioma\n",
      "glioma\n",
      "notumor\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "94765736/94765736 [==============================] - 3s 0us/step\n",
      "179/179 [==============================] - 13s 30ms/step\n",
      "41/41 [==============================] - 2s 41ms/step\n",
      "Test accuracy: 0.7864225781845919\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import glob\n",
    "import cv2\n",
    "import os\n",
    "from sklearn import preprocessing\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tensorflow.keras.applications.resnet50 import ResNet50\n",
    "from keras.models import Model\n",
    "\n",
    "# Set image size\n",
    "SIZE = 128\n",
    "\n",
    "# Load and preprocess training data\n",
    "train_images = []\n",
    "train_labels = [] \n",
    "\n",
    "for directory_path in glob.glob(\"/content/Tumor/Training/*\"):\n",
    "    label = directory_path.split(\"/\")[-1]\n",
    "    print(label)\n",
    "    for img_path in glob.glob(os.path.join(directory_path, \"*.png\")):\n",
    "        img = cv2.imread(img_path, cv2.IMREAD_COLOR)       \n",
    "        img = cv2.resize(img, (SIZE, SIZE))\n",
    "        train_images.append(img)\n",
    "        train_labels.append(label)\n",
    "\n",
    "train_images = np.array(train_images)\n",
    "train_labels = np.array(train_labels)\n",
    "\n",
    "# Load and preprocess testing data\n",
    "test_images = []\n",
    "test_labels = [] \n",
    "for directory_path in glob.glob(\"/content/Tumor/Testing/*\"):\n",
    "    label = directory_path.split(\"/\")[-1]\n",
    "    print(label)\n",
    "    for img_path in glob.glob(os.path.join(directory_path, \"*.png\")):\n",
    "        img = cv2.imread(img_path, cv2.IMREAD_COLOR)\n",
    "        img = cv2.resize(img, (SIZE, SIZE))\n",
    "        test_images.append(img)\n",
    "        test_labels.append(label)\n",
    "\n",
    "test_images = np.array(test_images)\n",
    "test_labels = np.array(test_labels)\n",
    "\n",
    "# Extract features from images using a pre-trained ResNet50 model\n",
    "resnet = ResNet50(weights='imagenet', include_top=False, input_shape=(SIZE, SIZE, 3))\n",
    "model = Model(inputs=resnet.input, outputs=resnet.output)\n",
    "\n",
    "train_features = model.predict(train_images)\n",
    "test_features = model.predict(test_images)\n",
    "\n",
    "# Reshape features to have one feature vector per image\n",
    "train_features = np.reshape(train_features, (train_features.shape[0], -1))\n",
    "test_features = np.reshape(test_features, (test_features.shape[0], -1))\n",
    "\n",
    "# Encode labels using LabelEncoder\n",
    "le = preprocessing.LabelEncoder()\n",
    "le.fit(test_labels)\n",
    "test_labels_encoded = le.transform(test_labels)\n",
    "le.fit(train_labels)\n",
    "train_labels_encoded = le.transform(train_labels)\n",
    "\n",
    "# Train Gaussian Naive Bayes model on extracted features\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(train_features, train_labels_encoded)\n",
    "\n",
    "# Make predictions on test set using extracted features\n",
    "y_pred = gnb.predict(test_features)\n",
    "\n",
    "# Compute accuracy score\n",
    "accuracy = accuracy_score(test_labels_encoded, y_pred)\n",
    "print('Test accuracy:', accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sx5kKa2VVNQJ",
    "outputId": "773d04e7-1bad-40aa-cf80-006fa3a2872d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pituitary\n",
      "meningioma\n",
      "glioma\n",
      "notumor\n",
      "pituitary\n",
      "meningioma\n",
      "glioma\n",
      "notumor\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "58889256/58889256 [==============================] - 0s 0us/step\n",
      "179/179 [==============================] - 15s 39ms/step\n",
      "41/41 [==============================] - 3s 67ms/step\n",
      "Test accuracy: 0.7574370709382151\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import glob\n",
    "import cv2\n",
    "import os\n",
    "from sklearn import preprocessing\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tensorflow.keras.applications.vgg16 import VGG16\n",
    "from keras.models import Model\n",
    "\n",
    "# Set image size\n",
    "SIZE = 128\n",
    "\n",
    "# Load and preprocess training data\n",
    "train_images = []\n",
    "train_labels = [] \n",
    "\n",
    "for directory_path in glob.glob(\"/content/Tumor/Training/*\"):\n",
    "    label = directory_path.split(\"/\")[-1]\n",
    "    print(label)\n",
    "    for img_path in glob.glob(os.path.join(directory_path, \"*.png\")):\n",
    "        img = cv2.imread(img_path, cv2.IMREAD_COLOR)       \n",
    "        img = cv2.resize(img, (SIZE, SIZE))\n",
    "        train_images.append(img)\n",
    "        train_labels.append(label)\n",
    "\n",
    "train_images = np.array(train_images)\n",
    "train_labels = np.array(train_labels)\n",
    "\n",
    "# Load and preprocess testing data\n",
    "test_images = []\n",
    "test_labels = [] \n",
    "for directory_path in glob.glob(\"/content/Tumor/Testing/*\"):\n",
    "    label = directory_path.split(\"/\")[-1]\n",
    "    print(label)\n",
    "    for img_path in glob.glob(os.path.join(directory_path, \"*.png\")):\n",
    "        img = cv2.imread(img_path, cv2.IMREAD_COLOR)\n",
    "        img = cv2.resize(img, (SIZE, SIZE))\n",
    "        test_images.append(img)\n",
    "        test_labels.append(label)\n",
    "\n",
    "test_images = np.array(test_images)\n",
    "test_labels = np.array(test_labels)\n",
    "\n",
    "# Extract features from images using a pre-trained ResNet50 model\n",
    "resnet = VGG16(weights='imagenet', include_top=False, input_shape=(SIZE, SIZE, 3))\n",
    "model = Model(inputs=resnet.input, outputs=resnet.output)\n",
    "\n",
    "train_features = model.predict(train_images)\n",
    "test_features = model.predict(test_images)\n",
    "\n",
    "# Reshape features to have one feature vector per image\n",
    "train_features = np.reshape(train_features, (train_features.shape[0], -1))\n",
    "test_features = np.reshape(test_features, (test_features.shape[0], -1))\n",
    "\n",
    "# Encode labels using LabelEncoder\n",
    "le = preprocessing.LabelEncoder()\n",
    "le.fit(test_labels)\n",
    "test_labels_encoded = le.transform(test_labels)\n",
    "le.fit(train_labels)\n",
    "train_labels_encoded = le.transform(train_labels)\n",
    "\n",
    "# Train Gaussian Naive Bayes model on extracted features\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(train_features, train_labels_encoded)\n",
    "\n",
    "# Make predictions on test set using extracted features\n",
    "y_pred = gnb.predict(test_features)\n",
    "\n",
    "# Compute accuracy score\n",
    "accuracy = accuracy_score(test_labels_encoded, y_pred)\n",
    "print('Test accuracy:', accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "g9faVRAcV5WG",
    "outputId": "9715a883-4012-43c6-9e47-1bab2ad7725b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pituitary\n",
      "meningioma\n",
      "glioma\n",
      "notumor\n",
      "pituitary\n",
      "meningioma\n",
      "glioma\n",
      "notumor\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "80134624/80134624 [==============================] - 4s 0us/step\n",
      "179/179 [==============================] - 17s 49ms/step\n",
      "41/41 [==============================] - 3s 81ms/step\n",
      "Test accuracy: 0.7803203661327232\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import glob\n",
    "import cv2\n",
    "import os\n",
    "from sklearn import preprocessing\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tensorflow.keras.applications.vgg19 import VGG19\n",
    "from keras.models import Model\n",
    "\n",
    "# Set image size\n",
    "SIZE = 128\n",
    "\n",
    "# Load and preprocess training data\n",
    "train_images = []\n",
    "train_labels = [] \n",
    "\n",
    "for directory_path in glob.glob(\"/content/Tumor/Training/*\"):\n",
    "    label = directory_path.split(\"/\")[-1]\n",
    "    print(label)\n",
    "    for img_path in glob.glob(os.path.join(directory_path, \"*.png\")):\n",
    "        img = cv2.imread(img_path, cv2.IMREAD_COLOR)       \n",
    "        img = cv2.resize(img, (SIZE, SIZE))\n",
    "        train_images.append(img)\n",
    "        train_labels.append(label)\n",
    "\n",
    "train_images = np.array(train_images)\n",
    "train_labels = np.array(train_labels)\n",
    "\n",
    "# Load and preprocess testing data\n",
    "test_images = []\n",
    "test_labels = [] \n",
    "for directory_path in glob.glob(\"/content/Tumor/Testing/*\"):\n",
    "    label = directory_path.split(\"/\")[-1]\n",
    "    print(label)\n",
    "    for img_path in glob.glob(os.path.join(directory_path, \"*.png\")):\n",
    "        img = cv2.imread(img_path, cv2.IMREAD_COLOR)\n",
    "        img = cv2.resize(img, (SIZE, SIZE))\n",
    "        test_images.append(img)\n",
    "        test_labels.append(label)\n",
    "\n",
    "test_images = np.array(test_images)\n",
    "test_labels = np.array(test_labels)\n",
    "\n",
    "# Extract features from images using a pre-trained ResNet50 model\n",
    "resnet = VGG19(weights='imagenet', include_top=False, input_shape=(SIZE, SIZE, 3))\n",
    "model = Model(inputs=resnet.input, outputs=resnet.output)\n",
    "\n",
    "train_features = model.predict(train_images)\n",
    "test_features = model.predict(test_images)\n",
    "\n",
    "# Reshape features to have one feature vector per image\n",
    "train_features = np.reshape(train_features, (train_features.shape[0], -1))\n",
    "test_features = np.reshape(test_features, (test_features.shape[0], -1))\n",
    "\n",
    "# Encode labels using LabelEncoder\n",
    "le = preprocessing.LabelEncoder()\n",
    "le.fit(test_labels)\n",
    "test_labels_encoded = le.transform(test_labels)\n",
    "le.fit(train_labels)\n",
    "train_labels_encoded = le.transform(train_labels)\n",
    "\n",
    "# Train Gaussian Naive Bayes model on extracted features\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(train_features, train_labels_encoded)\n",
    "\n",
    "# Make predictions on test set using extracted features\n",
    "y_pred = gnb.predict(test_features)\n",
    "\n",
    "# Compute accuracy score\n",
    "accuracy = accuracy_score(test_labels_encoded, y_pred)\n",
    "print('Test accuracy:', accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ACmzDZvsXSmH",
    "outputId": "3c9e015e-b41c-4108-a2e4-b97dafa174bc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pituitary\n",
      "meningioma\n",
      "glioma\n",
      "notumor\n",
      "pituitary\n",
      "meningioma\n",
      "glioma\n",
      "notumor\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "87910968/87910968 [==============================] - 5s 0us/step\n",
      "179/179 [==============================] - 13s 26ms/step\n",
      "41/41 [==============================] - 2s 41ms/step\n",
      "Test accuracy: 0.6323417238749046\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import glob\n",
    "import cv2\n",
    "import os\n",
    "from sklearn import preprocessing\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
    "from keras.models import Model\n",
    "\n",
    "# Set image size\n",
    "SIZE = 128\n",
    "\n",
    "# Load and preprocess training data\n",
    "train_images = []\n",
    "train_labels = [] \n",
    "\n",
    "for directory_path in glob.glob(\"/content/Tumor/Training/*\"):\n",
    "    label = directory_path.split(\"/\")[-1]\n",
    "    print(label)\n",
    "    for img_path in glob.glob(os.path.join(directory_path, \"*.png\")):\n",
    "        img = cv2.imread(img_path, cv2.IMREAD_COLOR)       \n",
    "        img = cv2.resize(img, (SIZE, SIZE))\n",
    "        train_images.append(img)\n",
    "        train_labels.append(label)\n",
    "\n",
    "train_images = np.array(train_images)\n",
    "train_labels = np.array(train_labels)\n",
    "\n",
    "# Load and preprocess testing data\n",
    "test_images = []\n",
    "test_labels = [] \n",
    "for directory_path in glob.glob(\"/content/Tumor/Testing/*\"):\n",
    "    label = directory_path.split(\"/\")[-1]\n",
    "    print(label)\n",
    "    for img_path in glob.glob(os.path.join(directory_path, \"*.png\")):\n",
    "        img = cv2.imread(img_path, cv2.IMREAD_COLOR)\n",
    "        img = cv2.resize(img, (SIZE, SIZE))\n",
    "        test_images.append(img)\n",
    "        test_labels.append(label)\n",
    "\n",
    "test_images = np.array(test_images)\n",
    "test_labels = np.array(test_labels)\n",
    "\n",
    "# Extract features from images using a pre-trained ResNet50 model\n",
    "resnet = InceptionV3(weights='imagenet', include_top=False, input_shape=(SIZE, SIZE, 3))\n",
    "model = Model(inputs=resnet.input, outputs=resnet.output)\n",
    "\n",
    "train_features = model.predict(train_images)\n",
    "test_features = model.predict(test_images)\n",
    "\n",
    "# Reshape features to have one feature vector per image\n",
    "train_features = np.reshape(train_features, (train_features.shape[0], -1))\n",
    "test_features = np.reshape(test_features, (test_features.shape[0], -1))\n",
    "\n",
    "# Encode labels using LabelEncoder\n",
    "le = preprocessing.LabelEncoder()\n",
    "le.fit(test_labels)\n",
    "test_labels_encoded = le.transform(test_labels)\n",
    "le.fit(train_labels)\n",
    "train_labels_encoded = le.transform(train_labels)\n",
    "\n",
    "# Train Gaussian Naive Bayes model on extracted features\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(train_features, train_labels_encoded)\n",
    "\n",
    "# Make predictions on test set using extracted features\n",
    "y_pred = gnb.predict(test_features)\n",
    "\n",
    "# Compute accuracy score\n",
    "accuracy = accuracy_score(test_labels_encoded, y_pred)\n",
    "print('Test accuracy:', accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "b8qLIyEUXx6q"
   },
   "outputs": [],
   "source": []
  }
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