braintumor_det.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "braintumor_det.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "6xwPPHQYfqO6"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "import numpy as np\n",
        "from google.colab.patches import cv2_imshow\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import  Conv2D ,MaxPool2D,Flatten,Dense\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vL9yoxYWfqAT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd7eb065-2ecb-4780-e67c-a523f14c320c"
      },
      "source": [
        "from google.colab import drive \n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3GlvsuhDfpj7"
      },
      "source": [
        "train_gen=ImageDataGenerator(rescale=1/.255,\n",
        "                             rotation_range=7,\n",
        "                             zoom_range=0.2,\n",
        "                             horizontal_flip=True)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zJMfew_3fpaB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38f4496b-3616-47ae-b88b-72a6975ca3d4"
      },
      "source": [
        "train_dataset=train_gen.flow_from_directory('/content/drive/MyDrive/computer vision/archive (4)/training',\n",
        "                                                     target_size=(64,64),batch_size=8,\n",
        "                                                     class_mode='categorical',\n",
        "                                                     shuffle=True)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 253 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nY1m7i9XfpQg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c2c1db0b-c276-4cf3-bc20-f9114806e03e"
      },
      "source": [
        "train_dataset.class_indices"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'no': 0, 'yes': 1}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xVN485tawoPQ",
        "outputId": "5b698116-44b5-46d1-e3fb-2bba9b7c4a57"
      },
      "source": [
        "(1152+2)/2 # choosing the number of neurons in the hidden layer w.r.t the vector size"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "577.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b0IlvSykfox1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f6c250c-04a1-4935-d294-5f33e8329d49"
      },
      "source": [
        "network=Sequential()\n",
        "network.add(Conv2D(filters=32,kernel_size=(3,3),activation='relu',input_shape=(64,64,3)))\n",
        "network.add(MaxPool2D(pool_size=(2,2)))\n",
        "\n",
        "network.add(Conv2D(filters=32,kernel_size=(3,3),activation='relu'))\n",
        "network.add(MaxPool2D(pool_size=(2,2)))\n",
        "\n",
        "network.add(Conv2D(filters=32,kernel_size=(3,3),activation='relu'))\n",
        "network.add(MaxPool2D(pool_size=(2,2)))\n",
        "\n",
        "network.add(Flatten())\n",
        "\n",
        "network.add(Dense(units=577, activation='relu'))\n",
        "network.add(Dense(units=577,activation='relu'))\n",
        "network.add(Dense(units=2,activation='softmax'))\n",
        "\n",
        "network.summary()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_3 (Conv2D)            (None, 62, 62, 32)        896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 31, 31, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 29, 29, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 12, 12, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 6, 6, 32)          0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 1152)              0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 577)               665281    \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 577)               333506    \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 2)                 1156      \n",
            "=================================================================\n",
            "Total params: 1,019,335\n",
            "Trainable params: 1,019,335\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WBvlAQfvsqfY"
      },
      "source": [
        "network.compile(optimizer='Adam',loss='categorical_crossentropy',metrics=['accuracy'])\n"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6VaR4fyNsqco",
        "outputId": "520eba1c-1126-49cc-9041-8b10175b4ce7"
      },
      "source": [
        "history=network.fit_generator(train_dataset,epochs=50)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "32/32 [==============================] - 3s 86ms/step - loss: 0.1456 - accuracy: 0.9526\n",
            "Epoch 2/50\n",
            "32/32 [==============================] - 3s 85ms/step - loss: 0.1061 - accuracy: 0.9684\n",
            "Epoch 3/50\n",
            "32/32 [==============================] - 3s 84ms/step - loss: 0.1168 - accuracy: 0.9447\n",
            "Epoch 4/50\n",
            "32/32 [==============================] - 3s 85ms/step - loss: 0.1816 - accuracy: 0.9447\n",
            "Epoch 5/50\n",
            "32/32 [==============================] - 3s 85ms/step - loss: 0.1449 - accuracy: 0.9644\n",
            "Epoch 6/50\n",
            "32/32 [==============================] - 3s 86ms/step - loss: 0.1206 - accuracy: 0.9565\n",
            "Epoch 7/50\n",
            "32/32 [==============================] - 3s 87ms/step - loss: 0.1721 - accuracy: 0.9447\n",
            "Epoch 8/50\n",
            "32/32 [==============================] - 3s 86ms/step - loss: 0.2670 - accuracy: 0.9368\n",
            "Epoch 9/50\n",
            "32/32 [==============================] - 3s 86ms/step - loss: 0.0554 - accuracy: 0.9842\n",
            "Epoch 10/50\n",
            "32/32 [==============================] - 3s 86ms/step - loss: 0.1070 - accuracy: 0.9605\n",
            "Epoch 11/50\n",
            "32/32 [==============================] - 3s 87ms/step - loss: 0.0740 - accuracy: 0.9763\n",
            "Epoch 12/50\n",
            "32/32 [==============================] - 3s 84ms/step - loss: 0.0700 - accuracy: 0.9723\n",
            "Epoch 13/50\n",
            "32/32 [==============================] - 3s 84ms/step - loss: 0.0322 - accuracy: 0.9881\n",
            "Epoch 14/50\n",
            "32/32 [==============================] - 3s 87ms/step - loss: 0.1998 - accuracy: 0.9526\n",
            "Epoch 15/50\n",
            "32/32 [==============================] - 3s 86ms/step - loss: 0.1716 - accuracy: 0.9407\n",
            "Epoch 16/50\n",
            "32/32 [==============================] - 3s 85ms/step - loss: 0.1152 - accuracy: 0.9486\n",
            "Epoch 17/50\n",
            "32/32 [==============================] - 3s 86ms/step - loss: 0.1284 - accuracy: 0.9526\n",
            "Epoch 18/50\n",
            "32/32 [==============================] - 3s 87ms/step - loss: 0.0548 - accuracy: 0.9842\n",
            "Epoch 19/50\n",
            "32/32 [==============================] - 3s 86ms/step - loss: 0.0756 - accuracy: 0.9605\n",
            "Epoch 20/50\n",
            "32/32 [==============================] - 3s 87ms/step - loss: 0.1308 - accuracy: 0.9526\n",
            "Epoch 21/50\n",
            "32/32 [==============================] - 3s 84ms/step - loss: 0.1006 - accuracy: 0.9605\n",
            "Epoch 22/50\n",
            "32/32 [==============================] - 3s 85ms/step - loss: 0.0782 - accuracy: 0.9723\n",
            "Epoch 23/50\n",
            "32/32 [==============================] - 3s 85ms/step - loss: 0.0763 - accuracy: 0.9802\n",
            "Epoch 24/50\n",
            "32/32 [==============================] - 3s 86ms/step - loss: 0.1585 - accuracy: 0.9605\n",
            "Epoch 25/50\n",
            "32/32 [==============================] - 3s 83ms/step - loss: 0.0905 - accuracy: 0.9644\n",
            "Epoch 26/50\n",
            "32/32 [==============================] - 3s 86ms/step - loss: 0.1864 - accuracy: 0.9289\n",
            "Epoch 27/50\n",
            "32/32 [==============================] - 3s 85ms/step - loss: 0.1584 - accuracy: 0.9486\n",
            "Epoch 28/50\n",
            "32/32 [==============================] - 3s 86ms/step - loss: 0.1215 - accuracy: 0.9605\n",
            "Epoch 29/50\n",
            "32/32 [==============================] - 3s 86ms/step - loss: 0.0499 - accuracy: 0.9842\n",
            "Epoch 30/50\n",
            "32/32 [==============================] - 3s 84ms/step - loss: 0.0944 - accuracy: 0.9684\n",
            "Epoch 31/50\n",
            "32/32 [==============================] - 3s 86ms/step - loss: 0.0578 - accuracy: 0.9684\n",
            "Epoch 32/50\n",
            "32/32 [==============================] - 3s 84ms/step - loss: 0.1064 - accuracy: 0.9644\n",
            "Epoch 33/50\n",
            "32/32 [==============================] - 3s 84ms/step - loss: 0.0604 - accuracy: 0.9802\n",
            "Epoch 34/50\n",
            "32/32 [==============================] - 3s 87ms/step - loss: 0.1950 - accuracy: 0.9289\n",
            "Epoch 35/50\n",
            "32/32 [==============================] - 3s 82ms/step - loss: 0.0795 - accuracy: 0.9684\n",
            "Epoch 36/50\n",
            "32/32 [==============================] - 3s 84ms/step - loss: 0.0570 - accuracy: 0.9763\n",
            "Epoch 37/50\n",
            "32/32 [==============================] - 3s 83ms/step - loss: 0.1533 - accuracy: 0.9684\n",
            "Epoch 38/50\n",
            "32/32 [==============================] - 3s 86ms/step - loss: 0.1662 - accuracy: 0.9486\n",
            "Epoch 39/50\n",
            "32/32 [==============================] - 3s 84ms/step - loss: 0.0856 - accuracy: 0.9684\n",
            "Epoch 40/50\n",
            "32/32 [==============================] - 3s 84ms/step - loss: 0.0584 - accuracy: 0.9723\n",
            "Epoch 41/50\n",
            "32/32 [==============================] - 3s 86ms/step - loss: 0.0583 - accuracy: 0.9881\n",
            "Epoch 42/50\n",
            "32/32 [==============================] - 3s 87ms/step - loss: 0.0180 - accuracy: 0.9960\n",
            "Epoch 43/50\n",
            "32/32 [==============================] - 3s 86ms/step - loss: 0.0625 - accuracy: 0.9802\n",
            "Epoch 44/50\n",
            "32/32 [==============================] - 3s 87ms/step - loss: 0.0432 - accuracy: 0.9842\n",
            "Epoch 45/50\n",
            "32/32 [==============================] - 3s 84ms/step - loss: 0.1319 - accuracy: 0.9763\n",
            "Epoch 46/50\n",
            "32/32 [==============================] - 3s 85ms/step - loss: 0.1311 - accuracy: 0.9644\n",
            "Epoch 47/50\n",
            "32/32 [==============================] - 3s 85ms/step - loss: 0.0585 - accuracy: 0.9802\n",
            "Epoch 48/50\n",
            "32/32 [==============================] - 3s 86ms/step - loss: 0.1143 - accuracy: 0.9526\n",
            "Epoch 49/50\n",
            "32/32 [==============================] - 3s 85ms/step - loss: 0.1328 - accuracy: 0.9763\n",
            "Epoch 50/50\n",
            "32/32 [==============================] - 3s 85ms/step - loss: 0.1194 - accuracy: 0.9684\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A-6tEzYEsqaQ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EtM33eu789Z-"
      },
      "source": [
        "def tumor(number):\n",
        "    \n",
        "    if number == 0:\n",
        "        \n",
        "        return \"Not a tumor\"\n",
        "    \n",
        "    else:\n",
        "        \n",
        "        return \"a tumor\""
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "t9VS3pOo7lMw",
        "outputId": "f3247901-1790-4cb2-b594-aad175ab61ac"
      },
      "source": [
        "from PIL import Image\n",
        "img = Image.open('/content/drive/MyDrive/computer vision/archive (4)/testing/brain_tumor_dataset/yes/Y100.JPG')\n",
        "\n",
        "x = np.array(img.resize((64,64)))\n",
        "\n",
        "x = x.reshape(1, 64, 64, 3)\n",
        "\n",
        "result = network.predict([x])\n",
        "\n",
        "classification = np.where(result == np.amax(result))[1][0]\n",
        "\n",
        "#\n",
        "print(str(result[0][classification]*100) + '% Confidence This Is ' + tumor(classification))\n",
        "\n",
        "plt.imshow(img)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "57.464033365249634% Confidence This Is a tumor\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f62e845b3d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        },
        {
          "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": "nQpGyKYjsqQE"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oIXSg-vwsqNk"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uWkFcQPAsqLG"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rNp2R1i7sqHk"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E4EK074GsqFo"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5aUwjXHisqDL"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zTWqNIb3sqA1"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TRJC7YGlsp9Z"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W2aQ01-ssp6K"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "siOneeUBsp1n"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "15sDu52sspta"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5UvZ-ZQqspqz"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m9Z6oTxSspnm"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XJ2PjQ7ZsplS"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "olDU0iJzspiL"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u3xDah6xspfq"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gJrr84xIspdS"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ByPD-UnHspap"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dIpi_uqQspXk"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VUz-tS9OspVE"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1rJeJlOspSo"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uvf-u9JUspQW"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0cuxWg-7spM_"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KbgwR-INspKC"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9o28XslLso9v"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}