Chest_Xray_COVID_Detection.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Chest-Xray-COVID-Detection.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "PD8qcIC83NN_",
        "colab_type": "code",
        "outputId": "e866dc86-5ffb-4ef7-c024-d0fb93d9e2d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Importing the Keras libraries and packages\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D\n",
        "from keras.layers import MaxPooling2D\n",
        "from keras.layers import Flatten\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Dropout\n",
        "import pickle\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OVZU5B_B3NBB",
        "colab_type": "code",
        "outputId": "4349a563-6f90-4b0c-f9cf-0254df4d9dd3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OVcjLVxv3M6X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Initialising the CNN\n",
        "classifier = Sequential()\n",
        "\n",
        "# Step 1 - Convolution\n",
        "classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))\n",
        "classifier.add(MaxPooling2D(pool_size = (2, 2)))\n",
        "\n",
        "# Adding a second convolutional layer\n",
        "classifier.add(Conv2D(32, (3, 3), activation = 'relu'))\n",
        "classifier.add(MaxPooling2D(pool_size = (2, 2)))\n",
        "\n",
        "# Adding a third convolutional layer\n",
        "classifier.add(Conv2D(32, (3, 3), activation = 'relu'))\n",
        "classifier.add(MaxPooling2D(pool_size = (2, 2)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vx1nXH7v3M0B",
        "colab_type": "code",
        "outputId": "ca4d09f6-34f4-4f6a-b0cc-39b456142983",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "# Step 3 - Flattening\n",
        "classifier.add(Flatten())\n",
        "\n",
        "# Step 4 - Full connection\n",
        "classifier.add(Dense(units = 64, activation = 'relu'))\n",
        "classifier.add(Dropout(p = 0.1))\n",
        "classifier.add(Dense(units = 128, activation = 'relu'))\n",
        "classifier.add(Dropout(p = 0.2))\n",
        "classifier.add(Dense(units = 1, activation = 'sigmoid'))\n",
        "\n",
        "# Compiling the CNN\n",
        "classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: UserWarning: Update your `Dropout` call to the Keras 2 API: `Dropout(rate=0.1)`\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: UserWarning: Update your `Dropout` call to the Keras 2 API: `Dropout(rate=0.2)`\n",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j8DC5akx3MtL",
        "colab_type": "code",
        "outputId": "e1deb9c8-893b-4b3f-a103-b709b2d8efa6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        }
      },
      "source": [
        "classifier.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 62, 62, 32)        896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 31, 31, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 29, 29, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 12, 12, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 6, 6, 32)          0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 1152)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 64)                73792     \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 128)               8320      \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 101,633\n",
            "Trainable params: 101,633\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rYDVAP6U3MqT",
        "colab_type": "code",
        "outputId": "31fbb79a-9b2c-4b41-a19a-1ff47730e0a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        }
      },
      "source": [
        "# Part 2 - Fitting the CNN to the images\n",
        "\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
        "                                   shear_range = 0.2,\n",
        "                                   zoom_range = 0.2,\n",
        "                                   horizontal_flip = True)\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale = 1./255)\n",
        "\n",
        "training_set = train_datagen.flow_from_directory('/content/drive/My Drive/data/train',\n",
        "                                                 target_size = (64, 64),\n",
        "                                                 batch_size = 32,\n",
        "                                                 class_mode = 'binary')\n",
        "\n",
        "test_set = test_datagen.flow_from_directory('/content/drive/My Drive/data/test',\n",
        "                                            target_size = (64, 64),\n",
        "                                            batch_size = 32,\n",
        "                                            class_mode = 'binary')\n",
        "\n",
        "history=classifier.fit_generator(training_set,\n",
        "                         steps_per_epoch =180,\n",
        "                         epochs = 6,\n",
        "                         validation_data = test_set,\n",
        "                         validation_steps = 48)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 180 images belonging to 2 classes.\n",
            "Found 48 images belonging to 2 classes.\n",
            "Epoch 1/6\n",
            "180/180 [==============================] - 445s 2s/step - loss: 0.2289 - accuracy: 0.8994 - val_loss: 0.0358 - val_accuracy: 0.9792\n",
            "Epoch 2/6\n",
            "180/180 [==============================] - 335s 2s/step - loss: 0.0996 - accuracy: 0.9639 - val_loss: 0.2742 - val_accuracy: 0.9583\n",
            "Epoch 3/6\n",
            "180/180 [==============================] - 336s 2s/step - loss: 0.0539 - accuracy: 0.9815 - val_loss: 0.0057 - val_accuracy: 0.9792\n",
            "Epoch 4/6\n",
            "180/180 [==============================] - 336s 2s/step - loss: 0.0371 - accuracy: 0.9881 - val_loss: 0.1180 - val_accuracy: 0.9583\n",
            "Epoch 5/6\n",
            "180/180 [==============================] - 337s 2s/step - loss: 0.0177 - accuracy: 0.9935 - val_loss: 0.0093 - val_accuracy: 0.9583\n",
            "Epoch 6/6\n",
            "180/180 [==============================] - 336s 2s/step - loss: 0.0192 - accuracy: 0.9930 - val_loss: 2.7138e-04 - val_accuracy: 0.9792\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x3EQFS263MmV",
        "colab_type": "code",
        "outputId": "b5c91455-b593-4da1-d6fc-e42ebd219f0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "#Summarising the Results\n",
        "import numpy as np\n",
        "\n",
        "a=list(history.history['accuracy'])\n",
        "b=list(history.history['val_accuracy'])\n",
        "max_train=100*np.round(max(a),4)\n",
        "max_validation=100*np.round(max(b),4)\n",
        "\n",
        "#Finding max of Training accuracy\n",
        "print(\"Maximum Training Accuracy is\",max_train)\n",
        "\n",
        "#Finding max  validation accuracy\n",
        "print(\"Maximum Validtion Accuracy is\",max_validation)\n",
        "        "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Maximum Training Accuracy is 99.34999942779541\n",
            "Maximum Validtion Accuracy is 97.92\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YrVQkXTM3Mkf",
        "colab_type": "code",
        "outputId": "735e5f8e-0de0-4f22-e686-6d1d799d1e4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        }
      },
      "source": [
        "#Plotting Test vs Train Accuracy\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model Accuracy(Test and Train)')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('No of Epochs')\n",
        "plt.legend(['Train', 'Test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "#Plotting Test vs Train Loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model Loss(Test and Train)')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('No of Epochs')\n",
        "plt.legend(['Train', 'Test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U6PTrBBh3MRt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# save the model to disk\n",
        "filename = 'final_corona_model.pkl'\n",
        "pickle.dump(classifier, open(filename, 'wb'))\n",
        "\n",
        "    \n",
        "# load the model from disk\n",
        "loaded_model = pickle.load(open(filename, 'rb'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KanFZTiD25F0",
        "colab_type": "code",
        "outputId": "277effe7-df53-421e-c6e2-d55939fcc32f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Making predictions using saved model\n",
        "\n",
        "from keras.preprocessing import image\n",
        "test_image = image.load_img('/content/drive/My Drive/data/val/sample.jpeg', target_size = (64, 64))\n",
        "test_image = image.img_to_array(test_image)\n",
        "test_image = np.expand_dims(test_image, axis = 0)\n",
        "result = loaded_model.predict(test_image)\n",
        "if result[0][0] == 1:\n",
        "    prediction = 'Corona'\n",
        "else:\n",
        "    prediction = 'Normal'\n",
        "print(prediction)\n",
        "\n",
        "#Corona"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Corona\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}