From e11da1dfea14cc1ee358855d201d1fd608733748 Mon Sep 17 00:00:00 2001 From: Shiza Charania <86497160+shizacharania@users.noreply.github.com> Date: Wed, 2 Feb 2022 20:21:33 -0500 Subject: [PATCH] Add files via upload --- Brain_Tumour_Classification.ipynb | 4820 +++++++++++++++++++++++++++++ 1 file changed, 4820 insertions(+) create mode 100644 Brain_Tumour_Classification.ipynb diff --git a/Brain_Tumour_Classification.ipynb b/Brain_Tumour_Classification.ipynb new file mode 100644 index 0000000..eaaf7a6 --- /dev/null +++ b/Brain_Tumour_Classification.ipynb @@ -0,0 +1,4820 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "MAyLN3L40fLX" + }, + "outputs": [], + "source": [ + "# install kaggle\n", + "!pip install -q kaggle" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 90, + "resources": { + "http://localhost:8080/nbextensions/google.colab/files.js": { + "data": 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+ "headers": [ + [ + "content-type", + "application/javascript" + ] + ], + "ok": true, + "status": 200, + "status_text": "" + } + } + }, + "id": "OvitX6-b0ozJ", + "outputId": "6ee8ecb3-50c5-48f8-cba6-b4ccc78942e2" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " Upload widget is only available when the cell has been executed in the\n", + " current browser session. Please rerun this cell to enable.\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saving kaggle.json to kaggle.json\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'kaggle.json': b'{\"username\":\"shizacharania\",\"key\":\"f4d4c61a9252e57ae14d3185e707df48\"}'}" + ] + }, + "metadata": {}, + "execution_count": 2 + } + ], + "source": [ + "# upload the generated api token in .json format from local system\n", + "from google.colab import files\n", + "files.upload()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "XyndXf2t0q2y" + }, + "outputs": [], + "source": [ + "# make a kaggle directory in my files\n", + "!mkdir ~/.kaggle" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "yFxsSFYw0s0h" + }, + "outputs": [], + "source": [ + "# put the kaggle.json file in that directory\n", + "!cp kaggle.json ~/.kaggle/" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "BkdhKkxi0ujM" + }, + "outputs": [], + "source": [ + "# grant permission for the .json file to act\n", + "!chmod 600 ~/.kaggle/kaggle.json" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "M3VcUxnA0wbW", + "outputId": "1dce1b25-33c8-43a0-9b55-515da8f2125d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)\n", + "ref title size lastUpdated downloadCount \n", + "---------------------------------------------------------- ------------------------------------------------- ----- ------------------- ------------- \n", + "nkitgupta/jigsaw-regression-based-data Jigsaw Regression Based Data 3GB 2022-01-10 06:29:59 676 \n", + "yamqwe/omicron-covid19-variant-daily-cases Omicron daily cases by country (COVID-19 variant) 432KB 2022-01-30 09:11:54 12099 \n", + "prasertk/netflix-subscription-price-in-different-countries Netflix subscription fee in different countries 3KB 2022-01-15 07:06:09 1691 \n", + "yasserh/wine-quality-dataset Wine Quality Dataset 21KB 2022-01-15 19:15:11 1508 \n", + "iamsouravbanerjee/analytics-industry-salaries-2022-india Data Professionals Salary - 2022 57KB 2022-02-02 04:35:21 1896 \n", + "yamqwe/netflix-showse Netflix Shows 11KB 2022-01-23 00:03:01 979 \n", + "sanjeetsinghnaik/top-1000-highest-grossing-movies Top 1000 Highest Grossing Movies 106KB 2022-01-15 16:26:14 1008 \n", + "yasserh/amazon-product-reviews-dataset Amazon Product Reviews Dataset 708KB 2022-01-23 17:21:16 499 \n", + "vishalmane10/anime-dataset-2022 Anime DataSet 2022 5MB 2022-01-16 13:56:16 799 \n", + "carlmcbrideellis/gdp-20152019-finland-norway-and-sweden GDP 2015-2019: Finland, Norway, and Sweden 365B 2022-01-05 07:48:49 542 \n", + "sandipdevre/petrol-prices-in-india Petrol Prices In India 3KB 2022-01-27 09:07:35 318 \n", + "yamqwe/shark-tank-companiese 📱 Shark Tank Companies 70KB 2022-01-30 21:01:58 184 \n", + "dansbecker/melbourne-housing-snapshot Melbourne Housing Snapshot 451KB 2018-06-05 12:52:24 79829 \n", + "yamqwe/men-s-shoe-pricese 🩰 Men's Shoe Prices 6MB 2022-01-24 15:19:48 405 \n", + "nenamalikah/nft-collections-by-sales-volume Top NFT Collections 47KB 2022-01-17 00:47:41 545 \n", + "meetnagadia/netflix-stock-price-data-set-20022022 Netflix Stock Price Data set 2002-2022 94KB 2022-01-12 05:28:11 507 \n", + "datasnaek/youtube-new Trending YouTube Video Statistics 201MB 2019-06-03 00:56:47 164782 \n", + "zynicide/wine-reviews Wine Reviews 51MB 2017-11-27 17:08:04 153488 \n", + "fedesoriano/stellar-classification-dataset-sdss17 Stellar Classification Dataset - SDSS17 7MB 2022-01-15 17:11:30 489 \n", + "rtatman/188-million-us-wildfires 1.88 Million US Wildfires 168MB 2020-05-12 21:03:49 18748 \n" + ] + } + ], + "source": [ + "# list available datsets to ensure that the API worked and kaggle's linked\n", + "!kaggle datasets list" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "uuey68EO05vZ", + "outputId": "42036609-1b36-417f-f016-ad40c8f4d0e8" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading brain-tumor-classification-mri.zip to /content\n", + " 93% 81.0M/86.8M [00:00<00:00, 109MB/s] \n", + "100% 86.8M/86.8M [00:00<00:00, 116MB/s]\n" + ] + } + ], + "source": [ + "# download the mri segmentation datset with its api command\n", + "!kaggle datasets download -d sartajbhuvaji/brain-tumor-classification-mri" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IoTxc8NB4q24", + "outputId": "2f791846-9aa7-4787-d346-9cb7c3af0a5b" + }, + "outputs": 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training images glioma tumor\n", + "tr_mt = glob.glob(\"/content/Training/meningioma_tumor/*\")\n", + "tr_none = glob.glob(\"/content/Training/no_tumor/*\")\n", + "tr_pt = glob.glob(\"/content/Training/pituitary_tumor/*\")\n", + "\n", + "te_gt = glob.glob(\"/content/Testing/glioma_tumor/*\") # training images glioma tumor\n", + "te_mt = glob.glob(\"/content/Testing/meningioma_tumor/*\")\n", + "te_none = glob.glob(\"/content/Testing/no_tumor/*\")\n", + "te_pt = glob.glob(\"/content/Testing/pituitary_tumor/*\")\n", + "\n", + "training_files = []\n", + "testing_files = []\n", + "\n", + "def appendimages(list_files, g_files, m_files, none_files, p_files):\n", + " for gt in g_files:\n", + " list_files.append(gt)\n", + " for mt in m_files:\n", + " list_files.append(mt)\n", + " for nonet in none_files:\n", + " list_files.append(nonet)\n", + " for pt in p_files:\n", + " list_files.append(pt)\n", + "\n", + "appendimages(training_files, tr_gt, tr_mt, tr_none, tr_pt)\n", + "appendimages(testing_files, te_gt, te_mt, te_none, te_pt)\n", + "\n", + "total_files = []\n", + "for one_file1 in training_files:\n", + " total_files.append(one_file1)\n", + "for one_file2 in testing_files:\n", + " total_files.append(one_file2)\n", + "\n", + "print(len(training_files), len(testing_files))\n", + "print(len(total_files))" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 355 + }, + "id": "XX6hm2jjqkBi", + "outputId": "59941dc4-1751-4830-c352-e0aac5c46a11" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "926 937 500 901\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 66 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "\n", + "total_gt = len(tr_gt) + len(te_gt)\n", + "total_mt = len(tr_mt) + len(te_mt)\n", + "total_none = len(tr_none) + len(te_none)\n", + "total_pt = len(tr_pt) + len(te_pt)\n", + "\n", + "print(total_gt, total_mt, total_none, total_pt)\n", + "total_images = total_gt + total_mt + total_none + total_pt\n", + "\n", + "fig = plt.figure(figsize = (8.5, 5))\n", + "\n", + "plt.bar([\"Glioma Tumours\", \"Meningioma Tumours\", \"No Tumours\", \"Pituitary Tumours\"], [total_gt, total_mt, total_none, total_pt], color=[\"orange\", \"blue\", \"green\", \"red\"])" + ] + }, + { + "cell_type": "code", + "source": [ + "file_shapes = 0\n", + "\n", + "for each_file in total_files:\n", + " a_file = cv2.imread(each_file)\n", + " shp = a_file.shape[0]\n", + " file_shapes += shp\n", + "\n", + "mean_file_size = file_shapes/len(total_files)\n", + "print(mean_file_size)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "aDkg5kG6K3gO", + "outputId": "bc41a6e4-ff30-4759-811e-67ec335465ef" + }, + "execution_count": 72, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "469.780943627451\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "adjLvIHV3zFb", + "outputId": "e487a008-db09-4704-94e6-3c15de144f73" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mean: tensor(0.0421)\n", + "Standard Deviation: tensor(2.1139)\n" + ] + } + ], + "source": [ + "import torch\n", + "\n", + "# find mean and standard deviation\n", + "num_pixels = total_images*mean_file_size*mean_file_size\n", + "\n", + "total_sum = 0\n", + "for each_file in total_files:\n", + " a_file = cv2.imread(each_file)\n", + " a_file = cv2.resize(a_file, (int(mean_file_size), int(mean_file_size)))\n", + " a_tensor = torch.from_numpy(a_file)\n", + " total_sum += a_tensor[0].sum() # there is a [0] because you only need one channel's sum, not all 3\n", + " mean = total_sum/num_pixels\n", + "\n", + "# std calculated with formula\n", + "sum_squared_error = 0\n", + "for each_file in total_files:\n", + " a_file = cv2.imread(each_file)\n", + " a_file = cv2.resize(a_file, (int(mean_file_size), int(mean_file_size)))\n", + " a_tensor = torch.from_numpy(a_file)\n", + " sum_squared_error += ((a_tensor[0]-mean)**2).sum()\n", + " std = (sum_squared_error/num_pixels)**0.5\n", + "\n", + "print(\"Mean:\", mean)\n", + "print(\"Standard Deviation:\", std)" + ] + }, + { + "cell_type": "code", + "execution_count": 410, + "metadata": { + "id": "9p50ET-GdZUx" + }, + "outputs": [], + "source": [ + "from torchvision.transforms.transforms import RandomRotation\n", + "import torch\n", + "import torchvision\n", + "import torchvision.transforms as transforms\n", + "\n", + "tr_image_data = []\n", + "teimage_data = []\n", + "tr_label_data = []\n", + "telabel_data = []\n", + "\n", + "def loading_data(lst, datalist, labellist, yn_transforms):\n", + " count = 0\n", + " for d in lst:\n", + " if \"glioma_tumor\" in d:\n", + " labellist.append(int(0))\n", + " elif \"meningioma_tumor\"in d:\n", + " labellist.append(int(1))\n", + " elif \"no_tumor\"in d:\n", + " labellist.append(int(2))\n", + " elif \"pituitary_tumor\"in d:\n", + " labellist.append(int(3))\n", + "\n", + " img = cv2.imread(str(d))\n", + " img = cv2.resize(img, (64,64))\n", + "\n", + " # transforms\n", + " if yn_transforms == True: # training\n", + " transform = transforms.Compose([transforms.ToTensor(), \n", + " transforms.RandomHorizontalFlip(p=0.5), \n", + " transforms.RandomVerticalFlip(p=0.5),\n", + " transforms.Normalize(mean, std)]) \n", + " img = transform(img)\n", + "\n", + " if count % 10 == 0:\n", + " another_transform = transforms.RandomRotation(degrees=(90))\n", + " img = another_transform(img)\n", + "\n", + " datalist.append(img)\n", + "\n", + " elif yn_transforms == False: # testing\n", + " transform = transforms.Compose([transforms.ToTensor(),\n", + " transforms.Normalize(mean, std)]) \n", + " img = transform(img)\n", + " datalist.append(img)\n", + " \n", + " count += 1\n" + ] + }, + { + "cell_type": "code", + "execution_count": 411, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NLsK8vbJidGR", + "outputId": "7c948ac8-c28e-4b0d-e71a-6df9962db465" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "torch.Size([3, 64, 64])\n" + ] + } + ], + "source": [ + "loading_data(training_files, tr_image_data, tr_label_data ,True)\n", + "print(tr_image_data[0].shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 412, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1RqwOBk2jMtY", + "outputId": "90e5e1d9-641d-4217-9c75-bad180ed8ef7" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "torch.Size([3, 64, 64])\n" + ] + } + ], + "source": [ + "loading_data(testing_files, teimage_data, telabel_data, False)\n", + "print(teimage_data[0].shape) " + ] + }, + { + "cell_type": "code", + "execution_count": 413, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BhZ3hVJ5bj27", + "outputId": "8f52fedd-842b-4808-e810-02a92a7fcc6a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2870 2870\n", + "394 394\n" + ] + } + ], + "source": [ + "print(len(tr_image_data), len(tr_label_data))\n", + "print(len(teimage_data), len(telabel_data))" + ] + }, + { + "cell_type": "code", + "execution_count": 414, + "metadata": { + "id": "NuqKffPsk-15" + }, + "outputs": [], + "source": [ + "len_trdataset = int(len(tr_image_data)*0.8) # 2296, but we use 2272 so it can divide evenly into the batch size\n", + "trimage_data = tr_image_data[:2272]\n", + "trlabel_data = tr_label_data[:2272]\n", + "\n", + "valid_len = (2870-2272) # 598 -> need 576\n", + "vimage_data = tr_image_data[2294:] \n", + "vlabel_data = tr_label_data[2294:]\n", + "\n", + "te_len = 0\n", + "teimage_data = teimage_data[:384] \n", + "telabel_data = telabel_data[:384]" + ] + }, + { + "cell_type": "code", + "execution_count": 415, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "U7CHjzBc3hV2", + "outputId": "0c5e59ad-3703-411d-aa36-f554894eb0df" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2272 2272\n", + "576 576\n", + "384 384\n" + ] + } + ], + "source": [ + "print(len(trimage_data), len(trlabel_data))\n", + "print(len(vimage_data), len(vlabel_data))\n", + "print(len(teimage_data), len(telabel_data))" + ] + }, + { + "cell_type": "code", + "source": [ + "import random\n", + "\n", + "training_data = list(zip(trimage_data, trlabel_data))\n", + "random.shuffle(training_data)\n", + "\n", + "validation_data = list(zip(vimage_data, vlabel_data))\n", + "random.shuffle(validation_data)\n", + "\n", + "\n", + "testing_data = list(zip(teimage_data, telabel_data))\n", + "random.shuffle(testing_data)\n", + "\n", + "print(len(training_data))\n", + "print(len(validation_data))\n", + "print(len(testing_data))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LrA_NVC86_IX", + "outputId": "f7736c27-d89b-4575-d67a-c76b5e9d862f" + }, + "execution_count": 416, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2272\n", + "576\n", + "384\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "trainloader = torch.utils.data.DataLoader(training_data, shuffle=True, batch_size=32)\n", + "validationloader = torch.utils.data.DataLoader(validation_data, shuffle=True, batch_size=32)\n", + "testingloader = torch.utils.data.DataLoader(testing_data, shuffle=True, batch_size=32)" + ], + "metadata": { + "id": "Esg9GHDF98QS" + }, + "execution_count": 417, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print(len(trainloader))\n", + "print(len(validationloader))\n", + "print(len(testingloader))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EPNUf55_Wa1Y", + "outputId": "615fcc84-a5f2-4333-bdeb-2b05e5fa3976" + }, + "execution_count": 418, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "71\n", + "18\n", + "12\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "categories = [\"glioma_tumor\", \"meningioma_tumor\", \"no_tumor\", \"pituitary_tumor\"]" + ], + "metadata": { + "id": "QWqlWASXmc2A" + }, + "execution_count": 419, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print(\"TRAINING:\")\n", + "trimages, trlabels = next(iter(trainloader))\n", + "print(trimages.shape)\n", + "print(len(trlabels))\n", + "trimages = trimages.permute(0,2,3,1)\n", + "plt.imshow(trimages[0])\n", + "plt.show()\n", + "print(categories[(trlabels[0])], \"\\n\")\n", + "\n", + "print(\"\\n\" + \"VALIDATION:\")\n", + "vimages, vlabels = next(iter(validationloader))\n", + "print(vimages.shape)\n", + "print(len(vlabels))\n", + "vimages = vimages.permute(0,2,3,1)\n", + "plt.imshow(vimages[0])\n", + "plt.show()\n", + "print(categories[(vlabels[0])], \"\\n\")\n", + "\n", + "print(\"\\n\" + \"TESTING:\")\n", + "teimages, telabels = next(iter(testingloader))\n", + "print(teimages.shape)\n", + "print(len(telabels))\n", + "teimages = teimages.permute(0,2,3,1)\n", + "plt.imshow(teimages[0])\n", + "plt.show()\n", + "print(categories[(telabels[0])])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "qOLgl28JXNml", + "outputId": "6a9786df-0a39-43ca-cc2f-83f5f055e8d2" + }, + "execution_count": 420, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "TRAINING:\n", + "torch.Size([32, 3, 64, 64])\n", + "32\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "pituitary_tumor \n", + "\n", + "\n", + "VALIDATION:\n", + "torch.Size([32, 3, 64, 64])\n", + "32\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "pituitary_tumor \n", + "\n", + "\n", + "TESTING:\n", + "torch.Size([32, 3, 64, 64])\n", + "32\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "no_tumor\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# visualize the images\n", + "nrows=4\n", + "ncols=10\n", + "images, labels = next(iter(trainloader))\n", + "images = images.permute((0,2,3,1))\n", + "\n", + "plt.figure(figsize=(20, 20))\n", + "for i in range(len(images[:10])):\n", + " plt.subplot(nrows, ncols, i+1)\n", + " plt.imshow(images[i])\n", + " plt.axis('off')\n", + " plt.subplots_adjust(wspace=0, hspace=0)\n", + "plt.show()\n", + "\n", + "plt.figure(figsize=(20, 20))\n", + "for j in labels[:10]:\n", + " print(categories[j], end=\"|\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 351 + }, + "id": "bXEX47wqbKYN", + "outputId": "dec558cb-badc-4f5e-cf70-bc42492fa28a" + }, + "execution_count": 421, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "meningioma_tumor|no_tumor|glioma_tumor|meningioma_tumor|no_tumor|glioma_tumor|pituitary_tumor|no_tumor|glioma_tumor|meningioma_tumor|" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "from torch.nn.modules.linear import Linear\n", + "from torch.nn.modules.batchnorm import BatchNorm2d\n", + "from torch import nn\n", + "\n", + "# model\n", + "# Using batch normalization makes the network more stable during training. \n", + "# This may require the use of much larger than normal learning rates, that in turn may further speed up the learning process.\n", + "\n", + "model = nn.Sequential(\n", + " # 64 x 64 x 3\n", + " nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1),\n", + " nn.ReLU(),\n", + " nn.BatchNorm2d(16),\n", + " # 64 x 64 x 16\n", + " nn.MaxPool2d(2,2),\n", + " # 32 x 32 x 16\n", + " nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1),\n", + " nn.ReLU(),\n", + " nn.BatchNorm2d(32),\n", + " # 32 x 32 x 32\n", + " nn.MaxPool2d(2,2),\n", + " # 16 x 16 x 32\n", + " nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),\n", + " nn.ReLU(),\n", + " nn.BatchNorm2d(64),\n", + " # # 16 x 16 x 64\n", + " nn.MaxPool2d(2,2),\n", + " # 8 x 8 x 64\n", + " # flatten\n", + " nn.Flatten(),\n", + " # 1 x 16384\n", + " nn.Linear(16*16*32, 512),\n", + " nn.ReLU(),\n", + " nn.Dropout(p=0.7),\n", + " nn.Linear(512,256),\n", + " nn.ReLU(),\n", + " nn.Dropout(p=0.6),\n", + " nn.Linear(256,4), # end output = number of classes\n", + " nn.Softmax(dim=1)) # for matrices, dim=1" + ], + "metadata": { + "id": "G44l3Bijgl0H" + }, + "execution_count": 521, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print(model)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PB-qy2909Faq", + "outputId": "9ef07215-baea-4c41-d254-740a6ccab49a" + }, + "execution_count": 522, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Sequential(\n", + " (0): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (1): ReLU()\n", + " (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (4): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (5): ReLU()\n", + " (6): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (7): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (8): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (9): ReLU()\n", + " (10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (11): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (12): Flatten(start_dim=1, end_dim=-1)\n", + " (13): Linear(in_features=8192, out_features=512, bias=True)\n", + " (14): ReLU()\n", + " (15): Dropout(p=0.7, inplace=False)\n", + " (16): Linear(in_features=512, out_features=256, bias=True)\n", + " (17): ReLU()\n", + " (18): Dropout(p=0.6, inplace=False)\n", + " (19): Linear(in_features=256, out_features=4, bias=True)\n", + " (20): Softmax(dim=1)\n", + ")\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# to check if the model works\n", + "inp = torch.rand(1,3,64,64)\n", + "print(inp.shape)\n", + "\n", + "out = model(inp)\n", + "print(out.shape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bOPDqpRJ9N8F", + "outputId": "7b10a66a-626f-4232-b7f1-66d26f3f3451" + }, + "execution_count": 519, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "torch.Size([1, 3, 64, 64])\n", + "torch.Size([1, 4])\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from torch import optim\n", + "import datetime\n", + "\n", + "optimizer = optim.Adam(model.parameters(), lr=0.0001)\n", + "criterion = nn.CrossEntropyLoss()" + ], + "metadata": { + "id": "-sRdottkpxJo" + }, + "execution_count": 473, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# training\n", + "\n", + "ttraining_losses = []\n", + "tvalidation_losses = []\n", + "\n", + "epochs = 30\n", + "\n", + "for epoch in range(epochs):\n", + " start_epoch = datetime.datetime.now()\n", + " training_loss = 0\n", + " validation_loss = 0\n", + " model.train()\n", + " for images, labels in trainloader: # each batch at a time\n", + " optimizer.zero_grad()\n", + " outputs = model(images)\n", + " loss = criterion(outputs, labels)\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " training_loss += loss.item() # loss.item() contains the loss of entire mini-batch, but divided by the batch size\n", + "\n", + " model.eval()\n", + "\n", + " for images, labels in validationloader: # each batch at a time\n", + " images, labels = next(iter(validationloader))\n", + " outputs = model(images)\n", + " loss = criterion(outputs, labels)\n", + "\n", + " validation_loss += loss.item()\n", + "\n", + " mean_trloss = training_loss/len(trainloader) # total loss from \"every batch/number of batches\"\n", + " mean_vloss = validation_loss/len(validationloader)\n", + "\n", + " ttraining_losses.append(mean_trloss)\n", + " tvalidation_losses.append(mean_vloss)\n", + "\n", + " print(\"Epoch: {} | Training Loss: {:.4f} | Validation Loss: {:.4f} |\".format(epoch+1, mean_trloss, mean_vloss))\n", + "\n", + " if mean_vloss <= min(tvalidation_losses):\n", + " print(\"Validation Loss has decreased - saving model...\")\n", + " torch.save(model.state_dict(), \"braintumorclassification.pth\")\n", + "\n", + " end_epoch = datetime.datetime.now()\n", + " print(\"Epoch time:\", str(end_epoch-start_epoch) + \"\\n\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sCuYLgce-YF_", + "outputId": "6391b919-fdf8-46f3-bf85-9c0fbbc89493" + }, + "execution_count": 478, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch: 1 | Training Loss: 1.0041 | Validation Loss: 1.0235 |\n", + "Validation Loss has decreased - saving model...\n", + "Epoch time: 0:00:10.516138\n", + "\n", + "Epoch: 2 | Training Loss: 0.9812 | Validation Loss: 1.0174 |\n", + "Validation Loss has decreased - saving model...\n", + "Epoch time: 0:00:10.587854\n", + "\n", + "Epoch: 3 | Training Loss: 0.9610 | Validation Loss: 1.0396 |\n", + "Epoch time: 0:00:10.575063\n", + "\n", + "Epoch: 4 | Training Loss: 0.9335 | Validation Loss: 1.0015 |\n", + "Validation Loss has decreased - saving model...\n", + "Epoch time: 0:00:10.728524\n", + "\n", + "Epoch: 5 | Training Loss: 0.9219 | Validation Loss: 0.9944 |\n", + "Validation Loss has decreased - saving model...\n", + "Epoch time: 0:00:10.503949\n", + "\n", + "Epoch: 6 | Training Loss: 0.9068 | Validation Loss: 0.9905 |\n", + "Validation Loss has decreased - saving model...\n", + "Epoch time: 0:00:10.401911\n", + "\n", + "Epoch: 7 | Training Loss: 0.8850 | Validation Loss: 0.9620 |\n", + "Validation Loss has decreased - saving model...\n", + "Epoch time: 0:00:10.744782\n", + "\n", + "Epoch: 8 | Training Loss: 0.8705 | Validation Loss: 1.0135 |\n", + "Epoch time: 0:00:10.654543\n", + "\n", + "Epoch: 9 | Training Loss: 0.8567 | Validation Loss: 0.9243 |\n", + "Validation Loss has decreased - saving model...\n", + "Epoch time: 0:00:10.551618\n", + "\n", + "Epoch: 10 | Training Loss: 0.8413 | Validation Loss: 0.9582 |\n", + "Epoch time: 0:00:10.377703\n", + "\n", + "Epoch: 11 | Training Loss: 0.8295 | Validation Loss: 0.9231 |\n", + "Validation Loss has decreased - saving model...\n", + "Epoch time: 0:00:10.415838\n", + "\n", + "Epoch: 12 | Training Loss: 0.8191 | Validation Loss: 0.9479 |\n", + "Epoch time: 0:00:10.555249\n", + "\n", + "Epoch: 13 | Training Loss: 0.8131 | Validation Loss: 0.9687 |\n", + "Epoch time: 0:00:10.573903\n", + "\n", + "Epoch: 14 | Training Loss: 0.8062 | Validation Loss: 1.0124 |\n", + "Epoch time: 0:00:10.342793\n", + "\n", + "Epoch: 15 | Training Loss: 0.8001 | Validation Loss: 0.9479 |\n", + "Epoch time: 0:00:10.566739\n", + "\n", + "Epoch: 16 | Training Loss: 0.7896 | Validation Loss: 0.9104 |\n", + "Validation Loss has decreased - saving model...\n", + "Epoch time: 0:00:10.519260\n", + "\n", + "Epoch: 17 | Training Loss: 0.7882 | Validation Loss: 0.9279 |\n", + "Epoch time: 0:00:10.310785\n", + "\n", + "Epoch: 18 | Training Loss: 0.7837 | Validation Loss: 1.0210 |\n", + "Epoch time: 0:00:10.405506\n", + "\n", + "Epoch: 19 | Training Loss: 0.7845 | Validation Loss: 0.9520 |\n", + "Epoch time: 0:00:10.468363\n", + "\n", + "Epoch: 20 | Training Loss: 0.7836 | Validation Loss: 0.9472 |\n", + "Epoch time: 0:00:10.342580\n", + "\n", + "Epoch: 21 | Training Loss: 0.7779 | Validation Loss: 1.0173 |\n", + "Epoch time: 0:00:10.753002\n", + "\n", + "Epoch: 22 | Training Loss: 0.7742 | Validation Loss: 0.9271 |\n", + "Epoch time: 0:00:10.762704\n", + "\n", + "Epoch: 23 | Training Loss: 0.7705 | Validation Loss: 0.9193 |\n", + "Epoch time: 0:00:10.311389\n", + "\n", + "Epoch: 24 | Training Loss: 0.7694 | Validation Loss: 0.9219 |\n", + "Epoch time: 0:00:10.356789\n", + "\n", + "Epoch: 25 | Training Loss: 0.7657 | Validation Loss: 0.9199 |\n", + "Epoch time: 0:00:10.266321\n", + "\n", + "Epoch: 26 | Training Loss: 0.7656 | Validation Loss: 0.9296 |\n", + "Epoch time: 0:00:10.269371\n", + "\n", + "Epoch: 27 | Training Loss: 0.7654 | Validation Loss: 0.9367 |\n", + "Epoch time: 0:00:10.366850\n", + "\n", + "Epoch: 28 | Training Loss: 0.7652 | Validation Loss: 0.8952 |\n", + "Validation Loss has decreased - saving model...\n", + "Epoch time: 0:00:10.317156\n", + "\n", + "Epoch: 29 | Training Loss: 0.7628 | Validation Loss: 0.8903 |\n", + "Validation Loss has decreased - saving model...\n", + "Epoch time: 0:00:10.228501\n", + "\n", + "Epoch: 30 | Training Loss: 0.7599 | Validation Loss: 0.8610 |\n", + "Validation Loss has decreased - saving model...\n", + "Epoch time: 0:00:10.199405\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.plot(ttraining_losses, label=\"Training loss\")\n", + "plt.plot(tvalidation_losses, label=\"Validation loss\")\n", + "plt.legend(frameon=False)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 284 + }, + "id": "KIwIBr2766O5", + "outputId": "7cf5bc12-769e-4306-8230-0905941a495a" + }, + "execution_count": 482, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 482 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "loaded_model = torch.load(\"braintumorclassification.pth\")\n", + "model.load_state_dict(loaded_model)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "a6kpbKmk7lhl", + "outputId": "7d172a99-9b04-492b-cdfb-76fa0e6d01c5" + }, + "execution_count": 483, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 483 + } + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "# testing\n", + "test_correct = [0,0,0,0]\n", + "test_total = [0,0,0,0]\n", + "ttesting_loss = 0\n", + "\n", + "for images, labels in testingloader: # each batch at a time\n", + " torch.no_grad() # disabled gradient calculation\n", + " outputs = model(images)\n", + " testing_loss = criterion(outputs, labels)\n", + "\n", + " ttesting_loss += testing_loss.item()\n", + "\n", + " probabilities = torch.exp(outputs) # output is the log probablities, so we use torch.exp to take the exponential (get the actual probabilites)\n", + " top_probabilities, top_categories = probabilities.topk(1, dim=1)\n", + "\n", + " for i in range(32): # from each batch\n", + " label = labels.data[i] # what label are we looking at\n", + " pred = top_categories[i]\n", + " if label == pred:\n", + " test_correct[label] += 1\n", + " test_total[label] += 1\n", + "\n", + "print(\"Testing Loss\", str(testing_loss/len(testingloader)), \"\\n\")\n", + "\n", + "gt_acc = (test_correct[0]/test_total[0])*100\n", + "mt_acc = (test_correct[1]/test_total[1])*100\n", + "nt_acc = (test_correct[2]/test_total[2])*100\n", + "pt_acc = (test_correct[3]/test_total[3])*100\n", + "\n", + "accuracy = (gt_acc + mt_acc + nt_acc + pt_acc)/4\n", + "\n", + "print(\"Overall Accuracy: {:.2f}%\".format(accuracy))\n", + "print(\"Accuracy of Glioma Tumour: {:.2f}%\".format(gt_acc))\n", + "print(\"Accuracy of Meningioma Tumour: {:.2f}%\".format(mt_acc))\n", + "print(\"Accuracy of No Tumour: {:.2f}%\".format(nt_acc))\n", + "print(\"Accuracy of Pituitary Tumour: {:.2f}%\".format(pt_acc))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZzdZVT4F-aib", + "outputId": "e8a36665-57b8-4b9d-a345-97e3346e76df" + }, + "execution_count": 485, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Testing Loss tensor(0.1012, grad_fn=) \n", + "\n", + "Overall Accuracy: 51.21%\n", + "Accuracy of Glioma Tumour: 16.00%\n", + "Accuracy of Meningioma Tumour: 68.70%\n", + "Accuracy of No Tumour: 90.48%\n", + "Accuracy of Pituitary Tumour: 29.69%\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# visualize the images\n", + "nrows=4\n", + "ncols=10\n", + "\n", + "visual_images, visual_labels = next(iter(testingloader))\n", + "\n", + "visual_images = visual_images[:5]\n", + "visual_labels = visual_labels[:5]\n", + "\n", + "for i in range(len(visual_images)):\n", + " image = visual_images[i]\n", + " label = visual_labels[i]\n", + " image = image.unsqueeze(1)\n", + " image = image.permute(1,0,2,3)\n", + " output = model(image)\n", + " probabilities = torch.exp(output)\n", + " top_probabilities, top_categories = probabilities.topk(1, dim=1)\n", + " print(\"Predicition:\", categories[top_categories] + \" \\n\" +\n", + " \"Label:\", categories[label])\n", + " image = image.squeeze()\n", + " image = image.permute(1,2,0)\n", + " plt.imshow(image)\n", + " plt.show()\n", + " print(\"\\n\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "ZnCkgPje8-hf", + "outputId": "3039301b-17e6-4411-94c9-c3b648f459d1" + }, + "execution_count": 518, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Predicition: glioma_tumor \n", + "Label: glioma_tumor\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "Predicition: meningioma_tumor \n", + "Label: meningioma_tumor\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "Predicition: no_tumor \n", + "Label: no_tumor\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Predicition: meningioma_tumor \n", + "Label: meningioma_tumor\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Predicition: no_tumor \n", + "Label: pituitary_tumor\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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