Assignment 3. X-ray image classification.ipynb

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        "# 3rd Assigment: Pneumonia Chest X-ray \n",
        "\n",
        "Data: https://www.kaggle.com/parthachakraborty/pneumonia-chest-x-ray/downloads/All.zip\n",
        "\n",
        "This zip file contains 5856 Chest X-Ray images as jpeg format and a CSV file(GTruth.csv). The CSV file contains ground truth information for images.\"0\" means Pneumonia case and \"1\" means normal case.\n",
        "\n",
        "The assigment has two parts:\n",
        "\n",
        "+ Developing a CNN model to classify X-ray images with high accuracy.\n",
        "+ To explore classifier explanation methods."
      ]
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      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import tensorflow as tf\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras import models\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras import applications\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.models import Sequential, Model \n",
        "from tensorflow.keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D,Conv2D,MaxPooling2D,BatchNormalization\n",
        "from tensorflow.keras import backend as k \n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping\n",
        "\n",
        "import tensorflow.keras\n",
        "from tensorflow.keras.optimizers import SGD\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n"
      ],
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      "outputs": []
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      "source": [
        "Download the data in a directory called `All`."
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        "df=pd.read_csv('All/GTruth.csv')\n",
        "df.head()"
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              " ...}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2019-05-07T10:08:31.976051Z",
          "start_time": "2019-05-07T10:07:28.504153Z"
        },
        "id": "BcGpjhlZ6Zfx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Reading Image data and converting it into pixels and separating class labels\n",
        "Data=[]\n",
        "Label=[]\n",
        "directory='All'\n",
        "\n",
        "for filename in os.listdir(directory) :\n",
        "    if filename.endswith(\".jpeg\") or filename.endswith(\".jpg\"):\n",
        "        \n",
        "        Label.append(kv_dict.get(int(filename.split('.')[0])))\n",
        "        filename=os.path.join(directory, filename)\n",
        "        im=image.load_img(filename,target_size=(224, 224))\n",
        "        im=np.reshape(im,(224,224,3))\n",
        "        im=im.astype('float32') / 255\n",
        "        Data.append(im)\n",
        "    else:\n",
        "        continue    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2019-05-07T10:09:19.468812Z",
          "start_time": "2019-05-07T10:08:59.598266Z"
        },
        "id": "9OHmY4W36Zfy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Train Test Split\n",
        "X_train, X_1, y_train, y_1 = train_test_split(np.array(Data), np.array(Label), test_size=0.2, random_state=42,stratify=Label)\n",
        "\n",
        "#Train Test Split\n",
        "X_cv, X_test, y_cv, y_test = train_test_split(X_1, y_1, test_size=0.2, random_state=42,stratify=y_1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2019-05-07T10:09:22.539208Z",
          "start_time": "2019-05-07T10:09:19.478665Z"
        },
        "id": "GFAygC2M6Zf0",
        "colab_type": "code",
        "colab": {},
        "outputId": "6d448111-6fc0-4933-8993-07d13f68be8a"
      },
      "source": [
        "img_width=224\n",
        "img_height=224\n",
        "\n",
        "from keras import backend as K\n",
        "\n",
        "if K.image_data_format() == 'channels_first':\n",
        "    input_shape = (3, img_width, img_height)\n",
        "    X_train=X_train.reshape(X_train.shape[0],3,img_width,img_height)\n",
        "    X_cv=X_cv.reshape(X_cv.shape[0],3,img_width,img_height)\n",
        "    X_test=X_test.reshape(X_test.shape[0],3,img_width,img_height)\n",
        "    \n",
        "else:\n",
        "    input_shape = (img_width, img_height, 3)\n",
        "    X_train=X_train.reshape(X_train.shape[0],img_width,img_height,3)\n",
        "    X_cv=X_cv.reshape(X_cv.shape[0],img_width,img_height,3)\n",
        "    X_test=X_test.reshape(X_test.shape[0],img_width,img_height,3)\n",
        "    \n",
        "del Data"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u5hWzjm06Zf2",
        "colab_type": "text"
      },
      "source": [
        "## Exercise 1\n",
        "\n",
        "+ Design and train a convolutional neural network for classifying X-ray images. \n",
        "+ Report the final (train and test) accuracy of your model.\n",
        "+ Plot the confusion matrix."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2019-05-07T10:09:44.872782Z",
          "start_time": "2019-05-07T10:09:44.867995Z"
        },
        "id": "3DB8IPNs6Zf4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# your code here"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D_lbqBN_6Zf5",
        "colab_type": "text"
      },
      "source": [
        "## Exercise 2\n",
        "\n",
        "SHAP (SHapley Additive exPlanations) (https://github.com/slundberg/shap) is a unified approach to explain the output of any machine learning model. SHAP connects game theory with local explanations, uniting several previous methods and representing the only possible consistent and locally accurate additive feature attribution method based on expectations.\n",
        "\n",
        "Some of the methods implemented in SHAP are designed to explain image classifications. Apply these methods to your model and make a critical review of their results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "55IgwdxI6Zf6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# your code here"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}