ResNet (partial dataset).ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "import PIL\n",
        "import cv2\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.pyplot import imshow\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.pyplot import imshow\n",
        "import shutil\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn.metrics import roc_curve,roc_auc_score\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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      "outputs": [
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "#pandas.read_csv(\"/content/drive/My Drive/folder_0/folder_1/file.csv\")\n",
        "train_df = pd.read_csv('/content/drive/Shareddrives/519 project/train_COVIDx9A.txt', sep=\" \", header=None)\n",
        "\n",
        "#Columns are added because it was seen that column names were 0,1,2,3, so new column names are added\n",
        "#which are given in descriptions\n",
        "train_df.columns=['patient id', 'filename', 'class', 'data source']\n",
        "\n",
        "# Since we are doing image classification, patient id and data source is of no importance to us, so\n",
        "#we cn drop them\n",
        "train_df=train_df.drop(['patient id', 'data source'], axis=1 )"
      ],
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        "train_df.head()"
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              "                                            filename      class\n",
              "0  SARS-10.1148rg.242035193-g04mr34g0-Fig8a-day0....  pneumonia\n",
              "1  SARS-10.1148rg.242035193-g04mr34g0-Fig8b-day5....  pneumonia\n",
              "2  SARS-10.1148rg.242035193-g04mr34g0-Fig8c-day10...  pneumonia\n",
              "3  SARS-10.1148rg.242035193-g04mr34g04a-Fig4a-day...  pneumonia\n",
              "4  SARS-10.1148rg.242035193-g04mr34g04b-Fig4b-day...  pneumonia"
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              "      <td>SARS-10.1148rg.242035193-g04mr34g0-Fig8a-day0....</td>\n",
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              "    <tr>\n",
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              "      <td>pneumonia</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SARS-10.1148rg.242035193-g04mr34g04a-Fig4a-day...</td>\n",
              "      <td>pneumonia</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>SARS-10.1148rg.242035193-g04mr34g04b-Fig4b-day...</td>\n",
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        "test_df = pd.read_csv('/content/drive/Shareddrives/519 project/test_COVIDx9A.txt', sep=\" \", header=None)\n",
        "test_df.columns=['id', 'filename', 'class', 'data source' ]\n",
        "test_df=test_df.drop(['id', 'data source'], axis=1 )"
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          "data": {
            "text/plain": [
              "                                    filename     class\n",
              "0  MIDRC-RICORD-1C-419639-003251-46647-0.png  COVID-19\n",
              "1  MIDRC-RICORD-1C-419639-001464-39871-0.png  COVID-19\n",
              "2  MIDRC-RICORD-1C-419639-000918-78965-0.png  COVID-19\n",
              "3  MIDRC-RICORD-1C-419639-003318-64285-0.png  COVID-19\n",
              "4  MIDRC-RICORD-1C-419639-001015-81591-0.png  COVID-19"
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              "      <th>4</th>\n",
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              "      <td>COVID-19</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "train_df['class'].value_counts()"
      ],
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        },
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pneumonia    5555\n",
              "normal       5555\n",
              "COVID-19     5555\n",
              "Name: class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "covid  = train_df[train_df['class']=='COVID-19']   #negative values in class column\n",
        "normal = train_df[train_df['class']=='normal']  #positive values in class column\n",
        "pneumonia = train_df[train_df['class']=='pneumonia']\n",
        "from sklearn.utils import resample\n",
        "#majority class that  is negative, we need to downsample/decrease that class so that there is no bias\n",
        "#n_samples = 2158 means we want 2158 sample of class negative, since there are 2158 samples of class positive\n",
        "df_covid_downsampled = resample(covid, replace = True, n_samples = 5555) \n",
        "df_normal_downsampled = resample(normal, replace = True, n_samples = 5555)\n",
        "#concatenate\n",
        "train_df = pd.concat([pneumonia, df_covid_downsampled,df_normal_downsampled])\n",
        "\n",
        "from sklearn.utils import shuffle\n",
        "train_df = shuffle(train_df) # shuffling so that there is particular sequence"
      ],
      "metadata": {
        "id": "BArjDggUt22-"
      },
      "execution_count": null,
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              "COVID-19     5555\n",
              "pneumonia    5555\n",
              "normal       5555\n",
              "Name: class, dtype: int64"
            ]
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          "metadata": {},
          "execution_count": 34
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      "source": [
        "test_df['class'].value_counts()"
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "COVID-19     200\n",
              "normal       100\n",
              "pneumonia    100\n",
              "Name: class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data, valid_data = train_test_split(train_df, train_size=0.8, random_state=0)"
      ],
      "metadata": {
        "id": "5G6i8a0jlltE"
      },
      "execution_count": null,
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    {
      "cell_type": "code",
      "source": [
        "train_path = '/content/drive/Shareddrives/519 project/train_30000/'  #directory path\n",
        "test_path = '/content/drive/Shareddrives/519 project/test.zip (Unzipped Files)'"
      ],
      "metadata": {
        "id": "-Hh6uXG0l1-z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#train_datagen = ImageDataGenerator(rescale = 1./255.,rotation_range = 40, width_shift_range = 0.2, height_shift_range = 0.2, \n",
        "                                   #shear_range = 0.2, zoom_range = 0.2, horizontal_flip = True, vertical_flip =True)\n",
        "train_datagen = ImageDataGenerator(rescale = 1./255., shear_range = 0.2)\n",
        "test_datagen = ImageDataGenerator(rescale = 1.0/255.)\n",
        "\n",
        "#Now fit the them to get the images from directory (name of the images are given in dataframe) with augmentation\n",
        "\n",
        "\n",
        "train_gen = train_datagen.flow_from_dataframe(dataframe = train_data, directory=train_path, x_col='filename', \n",
        "                                              y_col='class', target_size=(300,300), batch_size=64, \n",
        "                                               classes=['COVID-19','normal','pneumonia'])\n",
        "valid_gen = test_datagen.flow_from_dataframe(dataframe = valid_data, directory=train_path, x_col='filename',\n",
        "                                             y_col='class', target_size=(300,300), batch_size=64, \n",
        "                                            classes=['COVID-19','normal','pneumonia'])\n",
        "test_gen = test_datagen.flow_from_dataframe(dataframe = test_df, directory=test_path, x_col='filename', \n",
        "                                            y_col='class', target_size=(300,300), batch_size=32,\n",
        "                                             classes=['COVID-19','normal','pneumonia'])"
      ],
      "metadata": {
        "id": "q0Eg0OaYl2s5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "786e506e-ccac-43dd-c784-27424849e3f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras_preprocessing/image/dataframe_iterator.py:282: UserWarning: Found 10603 invalid image filename(s) in x_col=\"filename\". These filename(s) will be ignored.\n",
            "  .format(n_invalid, x_col)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2729 validated image filenames belonging to 3 classes.\n",
            "Found 650 validated image filenames belonging to 3 classes.\n",
            "Found 400 validated image filenames belonging to 3 classes.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras_preprocessing/image/dataframe_iterator.py:282: UserWarning: Found 2683 invalid image filename(s) in x_col=\"filename\". These filename(s) will be ignored.\n",
            "  .format(n_invalid, x_col)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "#Our base model is InceptionResNetV2, new readers are encouraged to see the architecture of this particular model\n",
        "\n",
        "base_model = tf.keras.applications.ResNet50V2(weights='imagenet', input_shape = (300,300,3),\n",
        "                                                     include_top=False,classifier_activation='softmax')\n",
        "for layer in base_model.layers:\n",
        "    layer.trainable = False"
      ],
      "metadata": {
        "id": "i5C9zO5jmE64",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ca71135-6a72-4ba0-b26c-5eeb74611448"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50v2_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94674944/94668760 [==============================] - 1s 0us/step\n",
            "94683136/94668760 [==============================] - 1s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plotLearningCurve(history,epochs):\n",
        "  epochRange = range(1,epochs+1)\n",
        "  fig, ax = plt.subplots(1,2,figsize=(20,10))\n",
        "  ax[0].plot(epochRange,history.history['accuracy'],'b',label = 'Training Accuracy')\n",
        "  ax[0].plot(epochRange,history.history['val_accuracy'],'r',label = 'Validation Accuracy')\n",
        "  ax[0].set_title('Training and Validation accuracy')\n",
        "  ax[0].set_xlabel('Epoch', fontsize = 20)\n",
        "  ax[0].set_ylabel('Accuracy', fontsize = 20)\n",
        "  ax[0].legend()\n",
        "  ax[0].grid(color='gray', linestyle='--')\n",
        "  ax[1].plot(epochRange,history.history['loss'],'b',label = 'Training Loss')\n",
        "  ax[1].plot(epochRange,history.history['val_loss'],'r',label = 'Validation Loss')\n",
        "  ax[1].set_title('Training and Validation loss')\n",
        "  ax[1].set_xlabel('Epoch', fontsize = 20)\n",
        "  ax[1].set_ylabel('Loss', fontsize = 20)\n",
        "  ax[1].legend()\n",
        "  ax[1].grid(color='gray', linestyle='--')\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "rWFeSIWLFGmD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Now we will add some more layers to the base model for our requirements\n",
        "\n",
        "model = tf.keras.Sequential([\n",
        "    base_model, \n",
        "    tf.keras.layers.GlobalAveragePooling2D(), \n",
        "    #tf.keras.layers.Conv2D(128, (3, 3), activation='relu', input_shape = (200,200,3)),\n",
        "    ##tf.keras.layers.MaxPool2D(pool_size=(2, 2), padding='valid'),\n",
        "    #tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128, activation='relu'),\n",
        "    tf.keras.layers.BatchNormalization(), \n",
        "    tf.keras.layers.Dropout(0.2), \n",
        "    tf.keras.layers.Dense(3, activation='softmax')\n",
        "])\n",
        "\n",
        "callbacks = [\n",
        "    tf.keras.callbacks.ModelCheckpoint(\"covid_classifier_model.h5\", save_best_only=True, verbose = 0),\n",
        "    tf.keras.callbacks.EarlyStopping(patience=3, monitor='val_loss', mode = \"min\", verbose=1),\n",
        "    tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=2, verbose=1)\n",
        "]\n",
        "\n",
        "model.compile(optimizer = keras.optimizers.Adam(learning_rate=0.001),\n",
        "              loss = 'categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "S-d9bN7rnT9v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.utils.class_weight import compute_class_weight\n",
        "\n",
        "weights = compute_class_weight(class_weight = \"balanced\", classes = np.unique(train_gen.classes), y = train_gen.classes)\n",
        "cw = dict(zip(np.unique(train_gen.classes), weights))\n"
      ],
      "metadata": {
        "id": "OPfCPmkYu644"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(train_gen, class_weight = cw,\n",
        "                    validation_data=valid_gen, epochs= 20, \n",
        "                    callbacks = callbacks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sco2XVaCn2VW",
        "outputId": "c6d49f8c-c35b-4485-e167-0975d0fc0aba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "43/43 [==============================] - 1925s 44s/step - loss: 0.4872 - accuracy: 0.8139 - val_loss: 0.4327 - val_accuracy: 0.8108 - lr: 0.0010\n",
            "Epoch 2/20\n",
            "43/43 [==============================] - 101s 2s/step - loss: 0.2696 - accuracy: 0.8985 - val_loss: 0.3200 - val_accuracy: 0.8815 - lr: 0.0010\n",
            "Epoch 3/20\n",
            "43/43 [==============================] - 99s 2s/step - loss: 0.2131 - accuracy: 0.9296 - val_loss: 0.3244 - val_accuracy: 0.8831 - lr: 0.0010\n",
            "Epoch 4/20\n",
            "43/43 [==============================] - ETA: 0s - loss: 0.1684 - accuracy: 0.9403\n",
            "Epoch 4: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
            "43/43 [==============================] - 101s 2s/step - loss: 0.1684 - accuracy: 0.9403 - val_loss: 0.3831 - val_accuracy: 0.8462 - lr: 0.0010\n",
            "Epoch 5/20\n",
            "43/43 [==============================] - 101s 2s/step - loss: 0.1200 - accuracy: 0.9648 - val_loss: 0.3037 - val_accuracy: 0.8769 - lr: 5.0000e-04\n",
            "Epoch 6/20\n",
            "43/43 [==============================] - 102s 2s/step - loss: 0.0979 - accuracy: 0.9733 - val_loss: 0.2777 - val_accuracy: 0.8954 - lr: 5.0000e-04\n",
            "Epoch 7/20\n",
            "43/43 [==============================] - 99s 2s/step - loss: 0.0809 - accuracy: 0.9791 - val_loss: 0.2806 - val_accuracy: 0.8923 - lr: 5.0000e-04\n",
            "Epoch 8/20\n",
            "43/43 [==============================] - 98s 2s/step - loss: 0.0722 - accuracy: 0.9813 - val_loss: 0.2616 - val_accuracy: 0.8923 - lr: 5.0000e-04\n",
            "Epoch 9/20\n",
            "43/43 [==============================] - 99s 2s/step - loss: 0.0610 - accuracy: 0.9846 - val_loss: 0.2928 - val_accuracy: 0.8877 - lr: 5.0000e-04\n",
            "Epoch 10/20\n",
            "43/43 [==============================] - ETA: 0s - loss: 0.0490 - accuracy: 0.9912\n",
            "Epoch 10: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
            "43/43 [==============================] - 100s 2s/step - loss: 0.0490 - accuracy: 0.9912 - val_loss: 0.3062 - val_accuracy: 0.8800 - lr: 5.0000e-04\n",
            "Epoch 11/20\n",
            "43/43 [==============================] - 100s 2s/step - loss: 0.0385 - accuracy: 0.9919 - val_loss: 0.2758 - val_accuracy: 0.8969 - lr: 2.5000e-04\n",
            "Epoch 11: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#model.load_weights('./covid_classifier_model.h5')\n",
        "model.evaluate(test_gen)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8EDzctKVJEaG",
        "outputId": "292dc2bc-6ff4-4968-8ce0-57e51d2912b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13/13 [==============================] - 335s 27s/step - loss: 0.6038 - accuracy: 0.7925\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6038158535957336, 0.7925000190734863]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(test_gen)"
      ],
      "metadata": {
        "id": "G-bpQHFR6iJu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_class=np.array([])\n",
        "prediction_class = np.array([])\n",
        "for i in range(13):\n",
        "   i, l = test_gen[i]\n",
        "   test_class = np.concatenate((test_class, np.argmax(l,axis=1)))\n",
        "   prediction_class = np.concatenate((prediction_class, np.argmax(model.predict(i),axis=1)))"
      ],
      "metadata": {
        "id": "cHFmqyEOazRP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(test_class,prediction_class)"
      ],
      "metadata": {
        "id": "HRCQs4_mbCUb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "cm = pd.DataFrame(cm , index = ['0','1','2'] , columns = ['0','1','2'])\n",
        "labels = ['COVID-19','normal','pneumonia']\n",
        "plt.figure(figsize = (10,10))\n",
        "sns.heatmap(cm,cmap= \"Blues\", linecolor = 'black' , linewidth = 1 , annot = True, fmt='',xticklabels = labels,yticklabels = labels)\n",
        "plt.title('Confusion Matrix of the ResNet Classification')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "id": "2-hHYuJ4cz9L",
        "outputId": "bf62ca78-06d2-4a7c-e9ba-7fa39d8ab315"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plotLearningCurve(history,11)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 650
        },
        "id": "8NdUeXgDXiMi",
        "outputId": "0e0f9a45-bbe1-4c4a-e91f-8810f878249d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}