Covid_19_Detector.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Covid-19 Detector",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1qrrschOZYegEAVykToy4TgotI5Sw5TPe",
      "authorship_tag": "ABX9TyPO95NIDYBGXx7+fhR+/0XD",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Rushikesh042/COVID-19-Detection-using-Deep-Learning/blob/master/Covid_19_Detector.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BW1OaBfgPcBm",
        "colab_type": "code",
        "outputId": "49a03713-4066-4e43-8ec6-06a3ef171c5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        }
      },
      "source": [
        "# Dataset : http://cb.lk/covid_19\n",
        "\n",
        "!wget http://cb.lk/covid_19\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-05-30 04:00:40--  http://cb.lk/covid_19\n",
            "Resolving cb.lk (cb.lk)... 159.89.170.74\n",
            "Connecting to cb.lk (cb.lk)|159.89.170.74|:80... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://cb.lk/covid_19 [following]\n",
            "--2020-05-30 04:00:41--  https://cb.lk/covid_19\n",
            "Connecting to cb.lk (cb.lk)|159.89.170.74|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://www.dropbox.com/s/7rjw6oet4za01op/CovidDataset-20200427T133042Z-001.zip?dl=0 [following]\n",
            "--2020-05-30 04:00:42--  https://www.dropbox.com/s/7rjw6oet4za01op/CovidDataset-20200427T133042Z-001.zip?dl=0\n",
            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.65.1, 2620:100:6021:1::a27d:4101\n",
            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.65.1|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: /s/raw/7rjw6oet4za01op/CovidDataset-20200427T133042Z-001.zip [following]\n",
            "--2020-05-30 04:00:42--  https://www.dropbox.com/s/raw/7rjw6oet4za01op/CovidDataset-20200427T133042Z-001.zip\n",
            "Reusing existing connection to www.dropbox.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://uc21fe018466923d1e7e63b7f9dc.dl.dropboxusercontent.com/cd/0/inline/A4rQrcl9rG0JU2XjU2Oxs0wp4RBXrWwOatmiTAXqHhDWakAnYfUP1NnrQRQEMcE9yOTk6WprFaIwPXw-Fa6DIXQTphmXpsFPtXlE_w5Jgbc-ACNkZs8gGI-gBXq5bKWQR5g/file# [following]\n",
            "--2020-05-30 04:00:43--  https://uc21fe018466923d1e7e63b7f9dc.dl.dropboxusercontent.com/cd/0/inline/A4rQrcl9rG0JU2XjU2Oxs0wp4RBXrWwOatmiTAXqHhDWakAnYfUP1NnrQRQEMcE9yOTk6WprFaIwPXw-Fa6DIXQTphmXpsFPtXlE_w5Jgbc-ACNkZs8gGI-gBXq5bKWQR5g/file\n",
            "Resolving uc21fe018466923d1e7e63b7f9dc.dl.dropboxusercontent.com (uc21fe018466923d1e7e63b7f9dc.dl.dropboxusercontent.com)... 162.125.65.6, 2620:100:6021:6::a27d:4106\n",
            "Connecting to uc21fe018466923d1e7e63b7f9dc.dl.dropboxusercontent.com (uc21fe018466923d1e7e63b7f9dc.dl.dropboxusercontent.com)|162.125.65.6|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 FOUND\n",
            "Location: /cd/0/inline2/A4oQ5Ycfqbsi22kO_x2IPo9uH2toTCqjtD1E9Yx_2yeBPwnF-S7efBcSyNjSoO1mGuavck4348505MRElVF5TDZ6S6Ev7SkTAnpStNC816kEfLAvcpLn1xQXW4CwdErJcc12VLd-K4laMcV5crEN_HJT9vM1eBpxQ4QFdVKU43YwOXLUj6OLGdGGq72hoKsHZhujFJaxwUhWEPaM0yFcF5Lskj_LBpedZLXOGT0qAl4TI3OwLDDubXeYifXdhPQ_mLpvAZPJn-nc5-83ovvhGslMMc2hgpnWO-UMnmQ8eLEmhjO2ZwWt3QaoTL_3KMDY_vybKYvfgOjajDeDFEWqu7qbY3Wm8Xh5JOppC7_UuS5wnQ/file [following]\n",
            "--2020-05-30 04:00:43--  https://uc21fe018466923d1e7e63b7f9dc.dl.dropboxusercontent.com/cd/0/inline2/A4oQ5Ycfqbsi22kO_x2IPo9uH2toTCqjtD1E9Yx_2yeBPwnF-S7efBcSyNjSoO1mGuavck4348505MRElVF5TDZ6S6Ev7SkTAnpStNC816kEfLAvcpLn1xQXW4CwdErJcc12VLd-K4laMcV5crEN_HJT9vM1eBpxQ4QFdVKU43YwOXLUj6OLGdGGq72hoKsHZhujFJaxwUhWEPaM0yFcF5Lskj_LBpedZLXOGT0qAl4TI3OwLDDubXeYifXdhPQ_mLpvAZPJn-nc5-83ovvhGslMMc2hgpnWO-UMnmQ8eLEmhjO2ZwWt3QaoTL_3KMDY_vybKYvfgOjajDeDFEWqu7qbY3Wm8Xh5JOppC7_UuS5wnQ/file\n",
            "Reusing existing connection to uc21fe018466923d1e7e63b7f9dc.dl.dropboxusercontent.com:443.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 140830621 (134M) [application/zip]\n",
            "Saving to: ‘covid_19’\n",
            "\n",
            "covid_19            100%[===================>] 134.31M  60.7MB/s    in 2.2s    \n",
            "\n",
            "2020-05-30 04:00:46 (60.7 MB/s) - ‘covid_19’ saved [140830621/140830621]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E6ZpAHrMuHPN",
        "colab_type": "code",
        "outputId": "9616f565-6ae5-4d21-fd69-d69923c6f01b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!unzip covid_19"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  covid_19\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MxhdU0NvwYYZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "TRAIN_PATH = \"CovidDataset/Train\"\n",
        "VAL_PATH = \"CovidDataset/Test\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "09FWCMmYwdtq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import keras\n",
        "from keras.layers import *\n",
        "from keras.models import * \n",
        "from keras.preprocessing import image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xQz-u0sswn8o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# CNN Based Model in Keras\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32,kernel_size=(3,3),activation='relu',input_shape=(224,224,3)))\n",
        "model.add(Conv2D(64,(3,3),activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(64,(3,3),activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(128,(3,3),activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(64,activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(1,activation='sigmoid'))\n",
        "\n",
        "model.compile(loss=keras.losses.binary_crossentropy,optimizer='adam',metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "czbkSCQMwpfc",
        "colab_type": "code",
        "outputId": "daf63804-57b6-4e22-f52e-a642140c5560",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        }
      },
      "source": [
        "model.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, 222, 222, 32)      896       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 220, 220, 64)      18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 110, 110, 64)      0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 110, 110, 64)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 108, 108, 64)      36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 54, 54, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 54, 54, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 52, 52, 128)       73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 26, 26, 128)       0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 26, 26, 128)       0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 86528)             0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 64)                5537856   \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 5,668,097\n",
            "Trainable params: 5,668,097\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GWkRqJlVw2Ou",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Train from scratch\n",
        "train_datagen = image.ImageDataGenerator(\n",
        "    rescale = 1./255,\n",
        "    shear_range = 0.2,\n",
        "    zoom_range = 0.2,\n",
        "    horizontal_flip = True,\n",
        ")\n",
        "\n",
        "test_dataset = image.ImageDataGenerator(rescale=1./255)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cY4UAa6Mw5Dw",
        "colab_type": "code",
        "outputId": "71d89227-9fb5-4ca5-f50b-c2f32ef35ef0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_generator = train_datagen.flow_from_directory(\n",
        "    'CovidDataset/Train',\n",
        "    target_size = (224,224),\n",
        "    batch_size = 32,\n",
        "    class_mode = 'binary')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 224 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "08j2sLvExAdC",
        "colab_type": "code",
        "outputId": "aaef5b7c-9a94-432a-ca54-b25a567730c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_generator.class_indices"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Covid': 0, 'Normal': 1}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VrEyGh4QxE-r",
        "colab_type": "code",
        "outputId": "a28f3e22-1b5a-435e-bb19-311165778041",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "validation_generator = test_dataset.flow_from_directory(\n",
        "    'CovidDataset/Val',\n",
        "    target_size = (224,224),\n",
        "    batch_size = 32,\n",
        "    class_mode = 'binary')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 60 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xwj74Tg_xJe8",
        "colab_type": "code",
        "outputId": "549dcbce-ef1d-4e59-8b67-5ca08e5d983c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        }
      },
      "source": [
        "hist = model.fit_generator(\n",
        "    train_generator,\n",
        "    steps_per_epoch=8,\n",
        "    epochs = 10,\n",
        "    validation_data = validation_generator,\n",
        "    validation_steps=2\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.8424 - accuracy: 0.5664 - val_loss: 0.6824 - val_accuracy: 0.9000\n",
            "Epoch 2/10\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.6087 - accuracy: 0.6562 - val_loss: 0.5391 - val_accuracy: 0.9333\n",
            "Epoch 3/10\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3731 - accuracy: 0.8242 - val_loss: 0.2820 - val_accuracy: 0.9333\n",
            "Epoch 4/10\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3578 - accuracy: 0.8477 - val_loss: 0.1832 - val_accuracy: 0.9500\n",
            "Epoch 5/10\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.2202 - accuracy: 0.9297 - val_loss: 0.1992 - val_accuracy: 0.9667\n",
            "Epoch 6/10\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.2208 - accuracy: 0.9375 - val_loss: 0.1928 - val_accuracy: 0.9667\n",
            "Epoch 7/10\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.2347 - accuracy: 0.9258 - val_loss: 0.1938 - val_accuracy: 0.9833\n",
            "Epoch 8/10\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.1672 - accuracy: 0.9531 - val_loss: 0.1142 - val_accuracy: 0.9667\n",
            "Epoch 9/10\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.1488 - accuracy: 0.9453 - val_loss: 0.0347 - val_accuracy: 0.9667\n",
            "Epoch 10/10\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.1104 - accuracy: 0.9453 - val_loss: 0.0220 - val_accuracy: 0.9833\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}