Covid_Detection_Model.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aAAybK6sX31k",
        "outputId": "d9e74ee9-9ca7-48a3-985a-e22dbd359b98"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "oBZ4fcqLYRkJ"
      },
      "outputs": [],
      "source": [
        "import numpy as np  # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import math\n",
        "import cv2\n",
        "import os\n",
        "#import umap\n",
        "import shutil\n",
        "import glob\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image\n",
        "from scipy import misc\n",
        "from os import listdir\n",
        "from os.path import isfile, join\n",
        "from random import shuffle\n",
        "from collections import Counter\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.manifold import TSNE\n",
        "import tensorflow as tf\n",
        "from keras.models import Sequential\n",
        "from keras.preprocessing import image\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.layers import Activation, Dropout, Flatten, Dense\n",
        "from keras.utils.np_utils import to_categorical"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "oz2Tc-pKbF6r",
        "outputId": "40a89a26-affe-4396-91ba-457eba52dc94"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nfor image in imgs:\\n  for count in glob.iglob(ROOT_DIR+image+\"*\"):\\n    counter[image] += 1\\n\\ncounter\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "counter = {'COVID':3616,'Normal':10192}\n",
        "\n",
        "'''\n",
        "for image in imgs:\n",
        "  for count in glob.iglob(ROOT_DIR+image+\"*\"):\n",
        "    counter[image] += 1\n",
        "\n",
        "counter\n",
        "'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "2J1GHKgiU6u5",
        "outputId": "213288b8-c241-4371-b43e-51e20faff3ca"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "plt.bar(x = counter.keys(), height= counter.values())\n",
        "plt.title(\"Covid: 3616 - Normal: 10192\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "YT26hg6zzlf-"
      },
      "outputs": [],
      "source": [
        "train_datagen = image.ImageDataGenerator(rescale=1/255, \n",
        "                                         horizontal_flip=True, zoom_range=0.2, \n",
        "                                         shear_range=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hnWWI1XB0kha",
        "outputId": "a318e9d0-10c0-4615-ea62-d89b33a4d75e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 410 images belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "train_data = train_datagen.flow_from_directory(directory='/content/drive/MyDrive/Train', \n",
        "                                               target_size=(256,256), batch_size=16, \n",
        "                                               class_mode='binary')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "US14JLC62Mq8",
        "outputId": "7466c5c5-c417-4772-d649-5a0ecb5df92f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Covid': 0, 'Normal': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "train_data.class_indices"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "RS7kUCcR2cjk"
      },
      "outputs": [],
      "source": [
        "test_datagen = image.ImageDataGenerator(rescale=1/255)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7xmrS88r2rn_",
        "outputId": "4210721f-5b39-4295-aa8a-ac8e3d2249cb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 170 images belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "test_data = test_datagen.flow_from_directory(directory='/content/drive/MyDrive/Test', \n",
        "                                               target_size=(256,256), batch_size=16,\n",
        "                                               class_mode='binary')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qBGvHOnGEJNR",
        "outputId": "a237beed-f4e6-494e-e49b-c1b558405083"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 254, 254, 32)      896       \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 252, 252, 64)      18496     \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 126, 126, 64)     0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 126, 126, 64)      0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 124, 124, 64)      36928     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 62, 62, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 62, 62, 64)        0         \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 60, 60, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 30, 30, 128)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 30, 30, 128)       0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 115200)            0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                7372864   \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 7,503,105\n",
            "Trainable params: 7,503,105\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(filters=32, kernel_size=(3,3), activation='relu', input_shape=(256,256,3)))\n",
        "\n",
        "model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu'))\n",
        "model.add(MaxPooling2D())\n",
        "model.add(Dropout(rate=0.25))\n",
        "\n",
        "model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu'))\n",
        "model.add(MaxPooling2D())\n",
        "model.add(Dropout(rate=0.25))\n",
        "\n",
        "model.add(Conv2D(filters=128, kernel_size=(3,3), activation='relu'))\n",
        "model.add(MaxPooling2D())\n",
        "model.add(Dropout(rate=0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(units=64, activation='relu'))\n",
        "model.add(Dropout(rate=0.50))\n",
        "model.add(Dense(units=1, activation='sigmoid'))\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "bc57AgeKF_4k"
      },
      "outputs": [],
      "source": [
        "model.compile(loss='binary_crossentropy', optimizer=\"adam\", metrics=['acc'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1LhNyh_7GUec",
        "outputId": "cbf5d784-ebb0-43fd-f2fe-21f7d790109b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "8/8 [==============================] - 49s 5s/step - loss: 2.6910 - acc: 0.5656 - val_loss: 0.7378 - val_acc: 0.5312\n",
            "Epoch 2/10\n",
            "8/8 [==============================] - 27s 4s/step - loss: 0.7515 - acc: 0.4531 - val_loss: 0.6878 - val_acc: 0.9375\n",
            "Epoch 3/10\n",
            "8/8 [==============================] - 22s 3s/step - loss: 0.6568 - acc: 0.6875 - val_loss: 0.6336 - val_acc: 0.6562\n",
            "Epoch 4/10\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.6482 - acc: 0.6250 - val_loss: 0.5906 - val_acc: 0.6250\n",
            "Epoch 5/10\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.6127 - acc: 0.6328 - val_loss: 0.4053 - val_acc: 0.7188\n",
            "Epoch 6/10\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.5201 - acc: 0.6557 - val_loss: 0.5303 - val_acc: 0.5312\n",
            "Epoch 7/10\n",
            "8/8 [==============================] - 4s 572ms/step - loss: 0.4628 - acc: 0.6967 - val_loss: 0.5130 - val_acc: 0.6562\n",
            "Epoch 8/10\n",
            "8/8 [==============================] - 4s 477ms/step - loss: 0.5462 - acc: 0.7377 - val_loss: 0.3593 - val_acc: 0.9688\n",
            "Epoch 9/10\n",
            "8/8 [==============================] - 4s 469ms/step - loss: 0.5298 - acc: 0.7422 - val_loss: 0.3697 - val_acc: 0.9375\n",
            "Epoch 10/10\n",
            "8/8 [==============================] - 3s 403ms/step - loss: 0.5021 - acc: 0.7500 - val_loss: 0.3300 - val_acc: 0.7500\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(train_data, steps_per_epoch=8, epochs=10, \n",
        "                    validation_steps=2, \n",
        "                    validation_data=test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gK2rECZuJjW7",
        "outputId": "9354ab92-84dc-4823-93b2-812827677f7d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'acc', 'val_loss', 'val_acc'])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "h = history.history\n",
        "h.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "lKxY_U4OLraL",
        "outputId": "cc91aa41-cb25-41ec-dc0d-9644d9f8f667"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(h['acc'])\n",
        "plt.plot(h['val_acc'] , c = \"red\")\n",
        "plt.title(\"Accuracy vs Valid Accuracy\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "kIPhmrP7zH-N",
        "outputId": "d2046dc1-f7a3-445d-d25d-ac3fd06ee582"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "epochs = range(1,11)\n",
        "plt.plot(epochs, val_acc, 'r', label='Validation Accuracy')\n",
        "plt.plot(epochs, acc, 'b', label='Accuracy')\n",
        "plt.title('Accuracy vs Validation Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "5CTGWrIvmzXe",
        "outputId": "e36381db-43eb-4bd8-f41c-bea771fc2d46"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "epochs = range(1,11)\n",
        "plt.plot(epochs, val_loss, 'r', label='Validation Loss')\n",
        "plt.plot(epochs, loss, 'b', label='Loss')\n",
        "plt.title('Loss vs Validation Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "rTjKmZzJyMtY",
        "outputId": "14b296ef-357d-4eee-bfbc-0c357ec49450"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(h['loss'])\n",
        "plt.plot(h['val_loss'] , c = \"red\")\n",
        "plt.title(\"Loss vs Valid Loss\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "WwtWT_Ru1KOh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d43a1120-7afc-45a9-99e8-83d89f6876ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 2s 212ms/step\n"
          ]
        }
      ],
      "source": [
        "predictions = model.predict(test_data, steps=8)\n",
        "# Get most likely class\n",
        "predicted_classes = np.argmax(predictions, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MligUN6b1ir_",
        "outputId": "dd79f15a-43d3-40da-a758-39c1635be529"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "predicted_classes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "Or7wH_ggzdi7"
      },
      "outputs": [],
      "source": [
        "true_classes = test_data.classes[:128]\n",
        "class_labels = list(test_data.class_indices.keys())  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WQ18EqhK04XP",
        "outputId": "841431ae-4f0a-482c-dcd2-0e4885262e0e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[70  0]\n",
            " [58  0]]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "print(confusion_matrix(true_classes, predicted_classes))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9WVAYyZB0752",
        "outputId": "b3fab0ce-703f-4204-da47-10737a78b6f2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Covid', 'Normal']"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "class_labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "niFwUOWN1owZ",
        "outputId": "bbd9e9a6-3370-47d7-c7e7-8142f1eb9ee6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.55      1.00      0.71        70\n",
            "           1       0.00      0.00      0.00        58\n",
            "\n",
            "    accuracy                           0.55       128\n",
            "   macro avg       0.27      0.50      0.35       128\n",
            "weighted avg       0.30      0.55      0.39       128\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ],
      "source": [
        "from sklearn import metrics\n",
        "report = metrics.classification_report(true_classes, predicted_classes)\n",
        "print(report)   "
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}