├── TIP model.JPG ├── README.md ├── TheModel.ipynb └── LICENSE.md /TIP model.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/namanbansalcodes/Brain-Tumor-Diagnosis-using-Deep-Learning/HEAD/TIP model.JPG -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Brain Tumor Diagnosis using Deep Learning 2 | 3 | **Dataset used:** https://figshare.com/articles/brain_tumor_dataset/1512427 4 | 5 | **Model used:** AlexNet 6 | 7 | **Trained on:** Google Collab with Python 3.8 8 | 9 | 10 | ## Problem Statement 11 | 12 | To build a deep neural network which can identify what kind of tumor exists in the brain. 13 | The model is supposed to classify brain tumors between: Meningloma, Glioma and Pitutary Tumors. 14 | 15 | ## Dataset 16 | 17 | Form the dataset mentioned above, we are using 2440 images as training set and rest 660 images as validation set. The dataset consists of 708 images for Meningloma, 18 | 1146 images for Glioma and 915 images for Pitutary Tumor. Also, the images in the dataset can belong to any of the three different views: Sagittal, Axial and Coronal Views (These views are nothing but different angle of of brain MRI). We have to train a model that can detect tumors accurately despite the views. 19 | 20 | NOTE: I have put all files from the dataset into one single folder in this project. 21 | 22 | ## Solution 23 | 24 | Our model is an implementation of AlexNet with 3 convolutoin layers and then 3 fully connected layers, as mentioned below: 25 | 26 | ![Alexnet Model used](https://github.com/namanbansalcodes/Brain-Tumor-Diagnosis-using-Deep-Learning/blob/master/TIP%20model.JPG?raw=true)! 27 | 28 | 29 | ## How to run 30 | 31 | Required Modules: Tensorflow 2.0, Numpy, h5py, Matplotlib.pyplot, os and cv2 32 | 33 | 1. Fetch the entire dataset to your google drive, unzip all folders and store them in one folder. (you can do this easily using https://www.multcloud.com/) 34 | 2. Open the notebook in Google Collab. 35 | 3. Mount Google Drive. 36 | 4. Do ya thing! 37 | 5. Hit me up on namanbansalcodes@gmail.com, let's talk. 38 | 39 | OR 40 | 41 | 1. Add my pickle file to your drive using this link: https://drive.google.com/file/d/1IKvwqGRjZajEf78I41j2VOdv_ztsQl6R/view?usp=sharing 42 | 2. Open the notebook in Google Collab. 43 | 3. Mount google drive and run the code block that says "load pickle file". 44 | 4. Do ya thing! 45 | 5. Enlighten me please. 46 | 47 | ## Conclusion 48 | 49 | When trained on 20 epochs with a batch size of 32, the model gives a training accuracy of 0.93 based on Sparse_Categorical_CrossEntropy loss function and 50 | it achieves a validation accuracy of 0.89 and a loss of around 0.23. 51 | 52 | Please help me improve the results. 53 | Hit me up at: namanbansalcodes@gmail.com 54 | -------------------------------------------------------------------------------- /TheModel.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "kernelspec": { 6 | "display_name": "Python 3.7.4 64-bit ('base': conda)", 7 | "language": "python", 8 | "name": "python37464bitbaseconda1b7c0a7557a44c1889c24da40cfb8365" 9 | }, 10 | "language_info": { 11 | "codemirror_mode": { 12 | "name": "ipython", 13 | "version": 3 14 | }, 15 | "file_extension": ".py", 16 | "mimetype": "text/x-python", 17 | "name": "python", 18 | "nbconvert_exporter": "python", 19 | "pygments_lexer": "ipython3", 20 | "version": "3.7.4" 21 | }, 22 | "colab": { 23 | "name": "Copy of TIP.ipynb", 24 | "provenance": [], 25 | "collapsed_sections": [] 26 | }, 27 | "accelerator": "GPU" 28 | }, 29 | "cells": [ 30 | { 31 | "cell_type": "code", 32 | "metadata": { 33 | "id": "4WmNMMqiUubS", 34 | "colab_type": "code", 35 | "colab": {} 36 | }, 37 | "source": [ 38 | "import os\n", 39 | "import numpy as np\n", 40 | "import matplotlib.pyplot as plt\n", 41 | "import h5py\n", 42 | "import cv2" 43 | ], 44 | "execution_count": null, 45 | "outputs": [] 46 | }, 47 | { 48 | "cell_type": "markdown", 49 | "metadata": { 50 | "id": "-SLRMNuAsn7h", 51 | "colab_type": "text" 52 | }, 53 | "source": [ 54 | "" 55 | ] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "metadata": { 60 | "id": "-Q_DdJygOC_t", 61 | "colab_type": "code", 62 | "colab": { 63 | "base_uri": "https://localhost:8080/", 64 | "height": 125 65 | }, 66 | "outputId": "7ab8d810-149f-42ad-8e40-afb8a73044aa" 67 | }, 68 | "source": [ 69 | "from google.colab import drive\n", 70 | "drive.mount('/content/drive')" 71 | ], 72 | "execution_count": null, 73 | "outputs": [ 74 | { 75 | "output_type": "stream", 76 | "text": [ 77 | "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n", 78 | "\n", 79 | "Enter your authorization code:\n", 80 | "··········\n", 81 | "Mounted at /content/drive\n" 82 | ], 83 | "name": "stdout" 84 | } 85 | ] 86 | }, 87 | { 88 | "cell_type": "code", 89 | "metadata": { 90 | "id": "PXPd50RvUubb", 91 | "colab_type": "code", 92 | "colab": {} 93 | }, 94 | "source": [ 95 | "#Converting all the data into Python list\n", 96 | "datadir=\"/content/drive/My Drive/Dataset\"\n", 97 | "N=3064\n", 98 | "traindata=[]\n", 99 | "\n", 100 | "for i in range(1,N+1):\n", 101 | " filename=str(i)+\".mat\"\n", 102 | " data=h5py.File(os.path.join(datadir,filename),\"r\")\n", 103 | " traindata.append(data)\n", 104 | "\n", 105 | " if i%100==0:\n", 106 | " print(filename)" 107 | ], 108 | "execution_count": null, 109 | "outputs": [] 110 | }, 111 | { 112 | "cell_type": "code", 113 | "metadata": { 114 | "id": "ddKDyINI0bMS", 115 | "colab_type": "code", 116 | "colab": { 117 | "base_uri": "https://localhost:8080/", 118 | "height": 34 119 | }, 120 | "outputId": "5f1df786-b273-43e7-8b94-bfd254bf847a" 121 | }, 122 | "source": [ 123 | "print(traindata[0])" 124 | ], 125 | "execution_count": null, 126 | "outputs": [ 127 | { 128 | "output_type": "stream", 129 | "text": [ 130 | "\n" 131 | ], 132 | "name": "stdout" 133 | } 134 | ] 135 | }, 136 | { 137 | "cell_type": "code", 138 | "metadata": { 139 | "id": "btuc3CQBy8nD", 140 | "colab_type": "code", 141 | "colab": { 142 | "base_uri": "https://localhost:8080/", 143 | "height": 195 144 | }, 145 | "outputId": "4991f76e-cbe8-4c9c-d8fb-e5ebb031aa5a" 146 | }, 147 | "source": [ 148 | "import random\n", 149 | "\n", 150 | "random.shuffle(traindata)" 151 | ], 152 | "execution_count": null, 153 | "outputs": [ 154 | { 155 | "output_type": "error", 156 | "ename": "NameError", 157 | "evalue": "ignored", 158 | "traceback": [ 159 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 160 | "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", 161 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraindata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", 162 | "\u001b[0;31mNameError\u001b[0m: name 'traindata' is not defined" 163 | ] 164 | } 165 | ] 166 | }, 167 | { 168 | "cell_type": "code", 169 | "metadata": { 170 | "scrolled": true, 171 | "id": "_NnThiOzUubh", 172 | "colab_type": "code", 173 | "colab": { 174 | "base_uri": "https://localhost:8080/", 175 | "height": 229 176 | }, 177 | "outputId": "f814f066-25e8-47ef-829b-8c9c1b972ea7" 178 | }, 179 | "source": [ 180 | "#Extracting image data from Python List\n", 181 | "trainx=[]\n", 182 | "testx=[]\n", 183 | "trainy=[]\n", 184 | "testy=[]\n", 185 | "\n", 186 | "temp=round(4*N/5) #using 4/5th of the data as training data\n", 187 | "\n", 188 | "#training data loop (trainx,trainy)\n", 189 | "for i in range(temp):\n", 190 | " image=traindata[i][\"cjdata\"][\"image\"].value\n", 191 | "\n", 192 | " if image.shape==(512,512):\n", 193 | " image=np.expand_dims(image,axis=0)\n", 194 | " trainx.append(image)\n", 195 | " \n", 196 | " label=int(traindata[i][\"cjdata\"][\"label\"].value)-1\n", 197 | " trainy.append(label)\n", 198 | "\n", 199 | "#testx and testy\n", 200 | "for i in range(temp,N):\n", 201 | " image=np.asarray(traindata[i][\"cjdata\"][\"image\"].value)\n", 202 | " \n", 203 | " if image.shape==(512,512):\n", 204 | " image=np.expand_dims(image,axis=0)\n", 205 | " testx.append(image)\n", 206 | " \n", 207 | " label=int(traindata[i][\"cjdata\"][\"label\"].value)-1\n", 208 | " testy.append(label)\n", 209 | "\n" 210 | ], 211 | "execution_count": null, 212 | "outputs": [ 213 | { 214 | "output_type": "error", 215 | "ename": "NameError", 216 | "evalue": "ignored", 217 | "traceback": [ 218 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 219 | "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", 220 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mtesty\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mtemp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#using 4/5th of the data as training data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;31m#training data loop (trainx,trainy)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 221 | "\u001b[0;31mNameError\u001b[0m: name 'N' is not defined" 222 | ] 223 | } 224 | ] 225 | }, 226 | { 227 | "cell_type": "code", 228 | "metadata": { 229 | "id": "dIs7iQqCUubl", 230 | "colab_type": "code", 231 | "colab": {} 232 | }, 233 | "source": [ 234 | "#Converting X list to numpy array #sentdexyoutube dogcats\n", 235 | "trainx=np.array(trainx).reshape(-1,512,512,1)\n", 236 | "testx=np.array(testx).reshape(-1,512,512,1)" 237 | ], 238 | "execution_count": null, 239 | "outputs": [] 240 | }, 241 | { 242 | "cell_type": "code", 243 | "metadata": { 244 | "id": "ON7xo5MzUubu", 245 | "colab_type": "code", 246 | "colab": {} 247 | }, 248 | "source": [ 249 | "#Converting Y list to numpy array\n", 250 | "trainy=np.array(trainy)\n", 251 | "testy=np.array(testy)" 252 | ], 253 | "execution_count": null, 254 | "outputs": [] 255 | }, 256 | { 257 | "cell_type": "code", 258 | "metadata": { 259 | "id": "p23IYTUkksGZ", 260 | "colab_type": "code", 261 | "colab": {} 262 | }, 263 | "source": [ 264 | "#Saving data as a pickle file (unnecessary step)\n", 265 | "import pickle\n", 266 | "braindata=[trainx,trainy,testx,testy]\n", 267 | "\n", 268 | "with open(\"newbraindata.pickle\",\"wb\") as file:\n", 269 | " pickle.dump(braindata,file)" 270 | ], 271 | "execution_count": null, 272 | "outputs": [] 273 | }, 274 | { 275 | "cell_type": "code", 276 | "metadata": { 277 | "id": "WCkHM4nUlcUU", 278 | "colab_type": "code", 279 | "colab": {} 280 | }, 281 | "source": [ 282 | "#Loading data from pickle file (unnecesary step if data has been fectched from drive)\n", 283 | "import pickle\n", 284 | "pickledir=\"/content/drive/My Drive/newbraindata.pickle\"\n", 285 | "trainx,trainy,testx,testy=pickle.load(open(pickledir,\"rb\"))" 286 | ], 287 | "execution_count": null, 288 | "outputs": [] 289 | }, 290 | { 291 | "cell_type": "code", 292 | "metadata": { 293 | "id": "BVMU9sTben6r", 294 | "colab_type": "code", 295 | "colab": {} 296 | }, 297 | "source": [ 298 | "np.random.seed(0)" 299 | ], 300 | "execution_count": null, 301 | "outputs": [] 302 | }, 303 | { 304 | "cell_type": "code", 305 | "metadata": { 306 | "id": "ymg-bS_uUub9", 307 | "colab_type": "code", 308 | "colab": {} 309 | }, 310 | "source": [ 311 | "#Model building starts\n", 312 | "import tensorflow as tf\n", 313 | "from tensorflow import keras\n", 314 | "from tensorflow.keras import layers\n", 315 | "\n", 316 | "tf.keras.backend.clear_session()" 317 | ], 318 | "execution_count": null, 319 | "outputs": [] 320 | }, 321 | { 322 | "cell_type": "code", 323 | "metadata": { 324 | "id": "wc-HKbI2UucX", 325 | "colab_type": "code", 326 | "colab": { 327 | "base_uri": "https://localhost:8080/", 328 | "height": 738 329 | }, 330 | "outputId": "99015a61-6f53-46be-9717-6da4203c66c1" 331 | }, 332 | "source": [ 333 | "#Initial BLock of the model\n", 334 | "ini_input=keras.Input(shape=(512,512,1),name=\"image\")\n", 335 | "\n", 336 | "x1=layers.Conv2D(64,(22,22),strides=2)(ini_input)\n", 337 | "x1=layers.MaxPooling2D((4,4))(x1)\n", 338 | "x1=layers.BatchNormalization()(x1)\n", 339 | "\n", 340 | "x2=layers.Conv2D(128,(11,11),strides=2,padding=\"same\")(x1)\n", 341 | "x2=layers.MaxPooling2D((2,2))(x2)\n", 342 | "x2=layers.BatchNormalization()(x2)\n", 343 | "\n", 344 | "x3=layers.Conv2D(256,(7,7),strides=2,padding=\"same\")(x2)\n", 345 | "x3=layers.MaxPooling2D((2,2))(x3)\n", 346 | "x3=layers.BatchNormalization()(x3)\n", 347 | "\n", 348 | "x4=layers.Flatten()(x3)\n", 349 | "x4=layers.Activation(\"relu\")(x4)\n", 350 | "\n", 351 | "x5=layers.Dense(1024,\"relu\")(x4)\n", 352 | "\n", 353 | "x6=layers.Dense(256,\"relu\")(x5)\n", 354 | "\n", 355 | "x9=layers.Dense(3)(x6)\n", 356 | "pred=layers.Activation(\"softmax\")(x9)\n", 357 | "\n", 358 | "model=keras.Model(inputs=ini_input,outputs=pred)\n", 359 | "\n", 360 | "model.compile(loss=\"sparse_categorical_crossentropy\",optimizer=\"adam\",metrics=[\"accuracy\"])\n", 361 | "model.fit(trainx,trainy,epochs=20,batch_size=32,shuffle=False)" 362 | ], 363 | "execution_count": null, 364 | "outputs": [ 365 | { 366 | "output_type": "stream", 367 | "text": [ 368 | "Epoch 1/20\n", 369 | "77/77 [==============================] - 25s 321ms/step - loss: 0.9946 - accuracy: 0.6426\n", 370 | "Epoch 2/20\n", 371 | "77/77 [==============================] - 25s 321ms/step - loss: 0.5693 - accuracy: 0.7471\n", 372 | "Epoch 3/20\n", 373 | "77/77 [==============================] - 25s 324ms/step - loss: 0.4501 - accuracy: 0.7971\n", 374 | "Epoch 4/20\n", 375 | "77/77 [==============================] - 25s 324ms/step - loss: 0.4175 - accuracy: 0.8139\n", 376 | "Epoch 5/20\n", 377 | "77/77 [==============================] - 25s 325ms/step - loss: 0.3460 - accuracy: 0.8500\n", 378 | "Epoch 6/20\n", 379 | "77/77 [==============================] - 25s 324ms/step - loss: 0.2860 - accuracy: 0.8873\n", 380 | "Epoch 7/20\n", 381 | "77/77 [==============================] - 25s 324ms/step - loss: 0.2268 - accuracy: 0.9057\n", 382 | "Epoch 8/20\n", 383 | "77/77 [==============================] - 25s 324ms/step - loss: 0.1864 - accuracy: 0.9250\n", 384 | "Epoch 9/20\n", 385 | "77/77 [==============================] - 25s 325ms/step - loss: 0.1307 - accuracy: 0.9512\n", 386 | "Epoch 10/20\n", 387 | "77/77 [==============================] - 25s 323ms/step - loss: 0.0869 - accuracy: 0.9689\n", 388 | "Epoch 11/20\n", 389 | "77/77 [==============================] - 25s 325ms/step - loss: 0.0994 - accuracy: 0.9652\n", 390 | "Epoch 12/20\n", 391 | "77/77 [==============================] - 25s 324ms/step - loss: 0.0950 - accuracy: 0.9639\n", 392 | "Epoch 13/20\n", 393 | "77/77 [==============================] - 25s 325ms/step - loss: 0.0730 - accuracy: 0.9713\n", 394 | "Epoch 14/20\n", 395 | "77/77 [==============================] - 25s 324ms/step - loss: 0.0424 - accuracy: 0.9861\n", 396 | "Epoch 15/20\n", 397 | "77/77 [==============================] - 25s 325ms/step - loss: 0.0658 - accuracy: 0.9795\n", 398 | "Epoch 16/20\n", 399 | "77/77 [==============================] - 25s 323ms/step - loss: 0.0294 - accuracy: 0.9885\n", 400 | "Epoch 17/20\n", 401 | "77/77 [==============================] - 25s 324ms/step - loss: 0.0588 - accuracy: 0.9799\n", 402 | "Epoch 18/20\n", 403 | "77/77 [==============================] - 25s 324ms/step - loss: 0.0179 - accuracy: 0.9922\n", 404 | "Epoch 19/20\n", 405 | "77/77 [==============================] - 25s 325ms/step - loss: 0.0158 - accuracy: 0.9939\n", 406 | "Epoch 20/20\n", 407 | "77/77 [==============================] - 25s 324ms/step - loss: 0.0463 - accuracy: 0.9885\n" 408 | ], 409 | "name": "stdout" 410 | }, 411 | { 412 | "output_type": "execute_result", 413 | "data": { 414 | "text/plain": [ 415 | "" 416 | ] 417 | }, 418 | "metadata": { 419 | "tags": [] 420 | }, 421 | "execution_count": 16 422 | } 423 | ] 424 | }, 425 | { 426 | "cell_type": "code", 427 | "metadata": { 428 | "id": "WRKz-ZoOUucb", 429 | "colab_type": "code", 430 | "colab": { 431 | "base_uri": "https://localhost:8080/", 432 | "height": 167 433 | }, 434 | "outputId": "954ea398-2876-443a-eb45-af6f038d23ec" 435 | }, 436 | "source": [ 437 | "model1=keras.models.save_model(\"CNNforbrainMRI.model\")" 438 | ], 439 | "execution_count": null, 440 | "outputs": [ 441 | { 442 | "output_type": "error", 443 | "ename": "NameError", 444 | "evalue": "ignored", 445 | "traceback": [ 446 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 447 | "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", 448 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"CNNforbrainMRI.model\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", 449 | "\u001b[0;31mNameError\u001b[0m: name 'keras' is not defined" 450 | ] 451 | } 452 | ] 453 | }, 454 | { 455 | "cell_type": "code", 456 | "metadata": { 457 | "id": "yFOMYNSCUucq", 458 | "colab_type": "code", 459 | "colab": { 460 | "base_uri": "https://localhost:8080/", 461 | "height": 52 462 | }, 463 | "outputId": "febe2e7e-8bed-43ec-82c8-0f8082bc2036" 464 | }, 465 | "source": [ 466 | "result=model.evaluate(testx,testy)\n", 467 | "print(result)" 468 | ], 469 | "execution_count": null, 470 | "outputs": [ 471 | { 472 | "output_type": "stream", 473 | "text": [ 474 | "20/20 [==============================] - 3s 146ms/step - loss: 0.4774 - accuracy: 0.8900\n", 475 | "[0.4773988425731659, 0.889983594417572]\n" 476 | ], 477 | "name": "stdout" 478 | } 479 | ] 480 | }, 481 | { 482 | "cell_type": "code", 483 | "metadata": { 484 | "id": "k5f3ANobdKJU", 485 | "colab_type": "code", 486 | "colab": { 487 | "base_uri": "https://localhost:8080/", 488 | "height": 140 489 | }, 490 | "outputId": "c70be0f5-6d61-40d6-c9dd-383f55f7b12b" 491 | }, 492 | "source": [ 493 | "!zip -r /content/CNNforbrainMRI.zip /content/CNNforbrainMRI.model" 494 | ], 495 | "execution_count": null, 496 | "outputs": [ 497 | { 498 | "output_type": "stream", 499 | "text": [ 500 | " adding: content/CNNforbrainMRI.model/ (stored 0%)\n", 501 | " adding: content/CNNforbrainMRI.model/assets/ (stored 0%)\n", 502 | " adding: content/CNNforbrainMRI.model/variables/ (stored 0%)\n", 503 | " adding: content/CNNforbrainMRI.model/variables/variables.data-00000-of-00002 (deflated 82%)\n", 504 | " adding: content/CNNforbrainMRI.model/variables/variables.index (deflated 71%)\n", 505 | " adding: content/CNNforbrainMRI.model/variables/variables.data-00001-of-00002 (deflated 17%)\n", 506 | " adding: content/CNNforbrainMRI.model/saved_model.pb (deflated 90%)\n" 507 | ], 508 | "name": "stdout" 509 | } 510 | ] 511 | }, 512 | { 513 | "cell_type": "code", 514 | "metadata": { 515 | "id": "VqPXcoTNvkc-", 516 | "colab_type": "code", 517 | "colab": { 518 | "base_uri": "https://localhost:8080/", 519 | "height": 52 520 | }, 521 | "outputId": "bbe75627-7a6d-47ce-c612-b5bb4bdb98c1" 522 | }, 523 | "source": [ 524 | "traincount=[0,0,0]\n", 525 | "testcount=[0,0,0]\n", 526 | "\n", 527 | "\n", 528 | "for i in range(len(trainy)):\n", 529 | " temp=trainy[i]\n", 530 | " traincount[temp]=traincount[temp]+1\n", 531 | "\n", 532 | "for i in range(len(testy)):\n", 533 | " temp=testy[i]\n", 534 | " testcount[temp]=testcount[temp]+1\n", 535 | " \n", 536 | "print(traincount)\n", 537 | "print(testcount)" 538 | ], 539 | "execution_count": null, 540 | "outputs": [ 541 | { 542 | "output_type": "stream", 543 | "text": [ 544 | "[564, 1147, 729]\n", 545 | "[144, 279, 186]\n" 546 | ], 547 | "name": "stdout" 548 | } 549 | ] 550 | } 551 | ] 552 | } 553 | -------------------------------------------------------------------------------- /LICENSE.md: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute 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Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------