pneumonia_detection.ipynb

{
  "metadata": {
    "kernelspec": {
      "language": "python",
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.7.12",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat_minor": 0,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/manny-uncharted/Deep-learning-projects/blob/main/pneumonia_detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8IbLCJJamY0L",
        "outputId": "d83fd9fb-3938-4fb8-baa4-3f2aad84c89d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip  /content/drive/MyDrive/chest-xray-pneumonia.zip  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Ch4qVHfnUvA",
        "outputId": "93efee82-24cd-4f7e-89dd-c5b3f4035ebe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import *"
      ],
      "metadata": {
        "id": "StluOnPLtdTg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras import models,layers,optimizers, Input\n",
        "from tensorflow.keras.layers import Dense, Activation,Dropout,Conv2D, MaxPooling2D,BatchNormalization, Flatten\n",
        "from tensorflow.keras.optimizers import Adam, Adamax\n",
        "from tensorflow.keras.models import Model, load_model, Sequential\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from time import time\n",
        "import cv2 as cv2\n",
        "from tqdm import tqdm\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import OneHotEncoder,LabelEncoder\n",
        "from matplotlib import pyplot as plt, cm  # plot creation\n",
        "import seaborn as sns\n",
        "from pathlib import Path\n",
        "sns.set_style('darkgrid')\n",
        "import warnings\n",
        "\n",
        "warnings.filterwarnings('ignore')\n",
        "# importing our models and layers \n",
        "from tensorflow.keras.applications import efficientnet, InceptionV3, DenseNet121,ResNet50\n",
        "from keras.layers import GlobalAveragePooling2D\n",
        "import glob\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from keras.layers import Input, Embedding, concatenate,Concatenate"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-12-20T19:35:34.976029Z",
          "iopub.execute_input": "2022-12-20T19:35:34.976383Z",
          "iopub.status.idle": "2022-12-20T19:35:42.498889Z",
          "shell.execute_reply.started": "2022-12-20T19:35:34.976300Z",
          "shell.execute_reply": "2022-12-20T19:35:42.497960Z"
        },
        "trusted": true,
        "id": "f2FZ0C7Zk9VR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_path = '/content/chest_xray/train'\n",
        "test_data_path = '/content/chest_xray/test'\n",
        "val_data_path = '/content/chest_xray/val'"
      ],
      "metadata": {
        "id": "FOmHuVPJ-pnO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## ----- Neural Network plot functions -----------##\n",
        "def plot_model_performance(history):\n",
        "    \"\"\"Plot model performance\"\"\"\n",
        "    plt.figure(figsize=(15, 8))\n",
        "    plt.subplot(2, 2, 1)\n",
        "    plt.plot(history.history['accuracy'], label = \"Training accuracy\")\n",
        "    plt.plot(history.history[\"val_accuracy\"], label=\"Validation accuracy\")\n",
        "    plt.legend()\n",
        "    plt.title(\"Training Accracy vs Validation Accuracy\")\n",
        "\n",
        "    plt.subplot(2,2,2)\n",
        "    plt.plot(history.history[\"loss\"], label = \"Training loss\")\n",
        "    plt.plot(history.history[\"val_loss\"], label=\"Validation loss\")\n",
        "    plt.legend()\n",
        "    plt.title(\"Training Loss Vs Validation Loss\")\n",
        "\n",
        "    plt.show()\n",
        "  \n",
        "### ------------ Model evalutaion  -----------###\n",
        "\n",
        "model_name = []\n",
        "recall = []\n",
        "precision = []\n",
        "F1 = []\n",
        "description = []\n",
        "\n",
        "def evaluate_model(y_test, y_preds):\n",
        "    \n",
        "    ## F1-Score\n",
        "    f1_eval = f1_score(y_test, y_preds, average='macro')\n",
        "    F1.append(f1_eval)\n",
        "    print(f'The F1-Score is: {f1_eval}')\n",
        "    \n",
        "    ## Precision score\n",
        "    precision_eval = precision_score(y_test, y_preds, average='macro') \n",
        "    precision.append(precision_eval)\n",
        "    print(f'The Precision-Score is: {precision_eval}')\n",
        "\n",
        "    ## Recall score\n",
        "    recall_eval = recall_score(y_test, y_preds, average='macro') \n",
        "    recall.append(recall_eval)\n",
        "    print(f'The Recall-Score is: {recall_eval}')"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-12-20T19:35:42.500906Z",
          "iopub.execute_input": "2022-12-20T19:35:42.501550Z",
          "iopub.status.idle": "2022-12-20T19:35:42.519714Z",
          "shell.execute_reply.started": "2022-12-20T19:35:42.501513Z",
          "shell.execute_reply": "2022-12-20T19:35:42.518691Z"
        },
        "trusted": true,
        "id": "ja1T0Oitk9VV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = ['NORMAL', 'PNEUMONIA']\n",
        "IMG_SIZE = 224"
      ],
      "metadata": {
        "id": "v41kOd7N36Gn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from random import shuffle"
      ],
      "metadata": {
        "id": "IRbI_lKORZO0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# read the data from the directory\n",
        "def get_data_from_folder(path):\n",
        "  imgs = []\n",
        "  classes  = []\n",
        "  for class_name in class_names: # Iterating through classes\n",
        "    print(path+\"/\"+class_name+\"/*\")\n",
        "    files = glob.glob(path+\"/\"+class_name+\"/*\") # get files in each folder(class)\n",
        "    for f in tqdm(files):\n",
        "      img = cv2.imread(f) #read the image\n",
        "      img = cv2.resize(img, (IMG_SIZE,IMG_SIZE)) # resize the image\n",
        "      imgs.append(np.array(img))\n",
        "      classes.append(class_names.index(class_name)) #Append images and corresponding labels to data\n",
        "    #except:\n",
        "     # pass\n",
        "    #np.random.shuffle(data)\n",
        "  shuff = np.arange(len(imgs)).astype(int)\n",
        "  np.random.shuffle(shuff)\n",
        "  imgs = [imgs[ind] for ind in shuff]\n",
        "  classes = [classes[ind] for ind in shuff]\n",
        "  return np.array(imgs).astype('float32'),np.array(classes)"
      ],
      "metadata": {
        "id": "tGv5O7qu4DE0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,y_train = get_data_from_folder(train_data_path)\n",
        "X_test,y_test = get_data_from_folder(test_data_path)\n",
        "X_val,y_val = get_data_from_folder(val_data_path)"
      ],
      "metadata": {
        "id": "547mIb_340kG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a353a700-76b4-452d-a98f-caa3da5dfc52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/chest_xray/train/NORMAL/*\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1341/1341 [00:35<00:00, 37.28it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/chest_xray/train/PNEUMONIA/*\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 3875/3875 [00:35<00:00, 109.49it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/chest_xray/test/NORMAL/*\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 234/234 [00:05<00:00, 41.10it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/chest_xray/test/PNEUMONIA/*\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 390/390 [00:03<00:00, 105.45it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/chest_xray/val/NORMAL/*\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 8/8 [00:00<00:00, 59.36it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/chest_xray/val/PNEUMONIA/*\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 8/8 [00:00<00:00, 135.21it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def resnet_model(no_layers = 1):\n",
        "    resnet_model = ResNet50(include_top=False, weights='imagenet')\n",
        "    for layer in resnet_model.layers[:-no_layers]:\n",
        "        layer._name = layer._name + '_resnet'\n",
        "        layer.trainable = False\n",
        "    resnet_x = resnet_model.output\n",
        "    x = GlobalAveragePooling2D()(resnet_x)\n",
        "    #  add a fully-connected dense llayer as head\n",
        "    x=Dropout(0.2)(x)\n",
        "    x = Dense(512, activation='relu')(x)\n",
        "    x=Dropout(0.2)(x)\n",
        "    x = Dense(64, activation='relu')(x)\n",
        "    x=Dropout(0.2)(x)\n",
        "    # and a logistic layer \n",
        "    predictions = Dense(1, activation='sigmoid')(x)\n",
        "    model = Model(inputs=resnet_model.input, outputs=predictions, name=\"resnet\")\n",
        "    model.compile(optimizer='adam', loss='binary_crossentropy',metrics=['acc'])\n",
        "    return model "
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-12-20T19:38:33.324627Z",
          "iopub.execute_input": "2022-12-20T19:38:33.324988Z",
          "iopub.status.idle": "2022-12-20T19:38:33.334114Z",
          "shell.execute_reply.started": "2022-12-20T19:38:33.324955Z",
          "shell.execute_reply": "2022-12-20T19:38:33.332844Z"
        },
        "trusted": true,
        "id": "lmH5n_NMk9VZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def inception_model(no_layers = 1):\n",
        "    inception_model=InceptionV3(include_top=False, weights='imagenet') # importation of model with pretrained weights and dropping the head\n",
        "    for layer in inception_model.layers: \n",
        "        layer._name = layer._name + '_inception' \n",
        "        layer.trainable = False # freezing the layers\n",
        "    inception_x = inception_model.output \n",
        "    x = GlobalAveragePooling2D()(inception_x)\n",
        "    #  add a fully-connected dense llayer as head\n",
        "    x=Dropout(0.2)(x)\n",
        "    x = Dense(512, activation='relu')(x)\n",
        "    x=Dropout(0.2)(x)\n",
        "    x = Dense(64, activation='relu')(x)\n",
        "    x=Dropout(0.2)(x)\n",
        "    # and a logistic layer \n",
        "    predictions = Dense(1, activation='sigmoid')(x)\n",
        "    model = Model(inputs=inception_model.input, outputs=predictions, name=\"inception\")\n",
        "    model.compile(optimizer='adam', loss='binary_crossentropy',metrics=['acc'])\n",
        "    return model"
      ],
      "metadata": {
        "id": "FootV23dCTFl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_results = []\n",
        "def run_model_NN (model,name):\n",
        "    callback = EarlyStopping(monitor='val_acc', patience=3)\n",
        "    start=time()\n",
        "    model.fit(X_train, y_train, validation_data=(X_test,y_test), batch_size=64, epochs=10,callbacks=[callback]) #, validation_data=(X_val, val_labels)\n",
        "    end_fit=time()\n",
        "    preds=[1 if x>0.5 else 0 for x in model.predict(X_test)]\n",
        "    end=time()\n",
        "    acc=accuracy_score(y_test,preds)\n",
        "    f1=f1_score(y_test,preds)\n",
        "    precision=precision_score(y_test,preds)\n",
        "    recall=recall_score(y_test,preds)\n",
        "    all_results.append([end_fit-start,end-end_fit,acc,f1,recall,precision,name])\n",
        "    print('scores for {}: accuracy: {:.4f} f1:{:.4f} recall:{:.4f} precision:{:.4f}'.format(name,acc,f1,recall,precision))"
      ],
      "metadata": {
        "id": "QP5QXKbTk9Va"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "run_model_NN(resnet_model(),'resnet')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4YfvHapek9Vb",
        "outputId": "fd88b958-127e-44da-ef94-33c3c6155561"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "82/82 [==============================] - 33s 252ms/step - loss: 0.1967 - acc: 0.9191 - val_loss: 0.4886 - val_acc: 0.8141\n",
            "Epoch 2/10\n",
            "82/82 [==============================] - 17s 207ms/step - loss: 0.1040 - acc: 0.9567 - val_loss: 0.7390 - val_acc: 0.7917\n",
            "Epoch 3/10\n",
            "82/82 [==============================] - 17s 208ms/step - loss: 0.0907 - acc: 0.9647 - val_loss: 0.8434 - val_acc: 0.7772\n",
            "Epoch 4/10\n",
            "82/82 [==============================] - 17s 211ms/step - loss: 0.0927 - acc: 0.9651 - val_loss: 0.4916 - val_acc: 0.8317\n",
            "Epoch 5/10\n",
            "82/82 [==============================] - 17s 212ms/step - loss: 0.0805 - acc: 0.9672 - val_loss: 0.5732 - val_acc: 0.8045\n",
            "Epoch 6/10\n",
            "82/82 [==============================] - 17s 212ms/step - loss: 0.0679 - acc: 0.9726 - val_loss: 0.6979 - val_acc: 0.8061\n",
            "Epoch 7/10\n",
            "82/82 [==============================] - 18s 217ms/step - loss: 0.0648 - acc: 0.9728 - val_loss: 0.5125 - val_acc: 0.8526\n",
            "Epoch 8/10\n",
            "82/82 [==============================] - 18s 218ms/step - loss: 0.0680 - acc: 0.9732 - val_loss: 0.7812 - val_acc: 0.8013\n",
            "Epoch 9/10\n",
            "82/82 [==============================] - 18s 216ms/step - loss: 0.0636 - acc: 0.9757 - val_loss: 0.5125 - val_acc: 0.8333\n",
            "Epoch 10/10\n",
            "82/82 [==============================] - 18s 217ms/step - loss: 0.0526 - acc: 0.9804 - val_loss: 0.7420 - val_acc: 0.8093\n",
            "20/20 [==============================] - 3s 122ms/step\n",
            "scores for resnet: accuracy: 0.8093 f1:0.8670 recall:0.9949 precision:0.7683\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "run_model_NN(inception_model(),'inception')"
      ],
      "metadata": {
        "id": "sWt1FL-DB0i4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results_df = pd.DataFrame(np.stack(all_results),columns =['train time','pred time','accuracy','f1_score','recall','precision','model'])\n",
        "results_df.round(3)\n",
        "results_df.to_csv('pneumonia results.csv', index = False)"
      ],
      "metadata": {
        "id": "UDKiKgnfCRuz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vchIYfqlBkrj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BKdfV6Tgvxz8"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}