Covid-19Xray.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Covid-19Xray.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyM7db+SdQHdIHciatUKIJIV"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"0HQNdMhYM-_2","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":125},"outputId":"2b941e2a-8f1f-41c0-eda3-fd09991e79b8","executionInfo":{"status":"ok","timestamp":1590349339995,"user_tz":-120,"elapsed":23773,"user":{"displayName":"vu ngoc minh on","photoUrl":"","userId":"08489535657617177327"}}},"source":["from google.colab import drive\n","import os\n","\n","drive.mount(\"/content/gdrive\")"],"execution_count":1,"outputs":[{"output_type":"stream","text":["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","\n","Enter your authorization code:\n","··········\n","Mounted at /content/gdrive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"DWshZsbFNTJD","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"7fb346a1-da0f-49aa-a7bf-9144b8517860","executionInfo":{"status":"ok","timestamp":1590349349206,"user_tz":-120,"elapsed":1606,"user":{"displayName":"vu ngoc minh on","photoUrl":"","userId":"08489535657617177327"}}},"source":["!ls"],"execution_count":2,"outputs":[{"output_type":"stream","text":["gdrive\tsample_data\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"MAY0n_jPNV5A","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"4fa9b91c-55d7-4a1b-a58f-6cbabb79aa28","executionInfo":{"status":"ok","timestamp":1590349376444,"user_tz":-120,"elapsed":438,"user":{"displayName":"vu ngoc minh on","photoUrl":"","userId":"08489535657617177327"}}},"source":["%cd '/content/gdrive/My Drive/deep_learning/covid-19'"],"execution_count":3,"outputs":[{"output_type":"stream","text":["/content/gdrive/My Drive/deep_learning/covid-19\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"XiPW6ZhzNcVO","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"3c447a86-58e9-4909-fa44-5374c1ef2594","executionInfo":{"status":"ok","timestamp":1590350272441,"user_tz":-120,"elapsed":664445,"user":{"displayName":"vu ngoc minh on","photoUrl":"","userId":"08489535657617177327"}}},"source":["# USAGE\n","# python train.py --dataset dataset\n","\n","# import the necessary packages\n","from tensorflow.keras.preprocessing.image import ImageDataGenerator\n","from tensorflow.keras.applications import VGG16\n","from tensorflow.keras.layers import AveragePooling2D\n","from tensorflow.keras.layers import Dropout\n","from tensorflow.keras.layers import Flatten\n","from tensorflow.keras.layers import Dense\n","from tensorflow.keras.layers import Input\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.utils import to_categorical\n","from sklearn.preprocessing import LabelBinarizer\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import classification_report\n","from sklearn.metrics import confusion_matrix\n","from imutils import paths\n","import matplotlib.pyplot as plt\n","import numpy as np\n","# import argparse\n","import cv2\n","import os\n","\n","# construct the argument parser and parse the arguments\n","# ap = argparse.ArgumentParser()\n","# ap.add_argument(\"-d\", \"--dataset\", required=True,\n","# \thelp=\"path to input dataset\")\n","# ap.add_argument(\"-p\", \"--plot\", type=str, default=\"plot.png\",\n","# \thelp=\"path to output loss/accuracy plot\")\n","# ap.add_argument(\"-m\", \"--model\", type=str, default=\"covid19.model\",\n","# \thelp=\"path to output loss/accuracy plot\")\n","# args = vars(ap.parse_args())\n","\n","# initialize the initial learning rate, number of epochs to train for,\n","# and batch size\n","INIT_LR = 1e-3\n","EPOCHS = 25\n","BS = 8\n","\n","# grab the list of images in our dataset directory, then initialize\n","# the list of data (i.e., images) and class images\n","print(\"[INFO] loading images...\")\n","imagePaths = list(paths.list_images(\"dataset\"))\n","data = []\n","labels = []\n","\n","# loop over the image paths\n","for imagePath in imagePaths:\n","\t# extract the class label from the filename\n","\tlabel = imagePath.split(os.path.sep)[-2]\n","\n","\t# load the image, swap color channels, and resize it to be a fixed\n","\t# 224x224 pixels while ignoring aspect ratio\n","\timage = cv2.imread(imagePath)\n","\timage = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n","\timage = cv2.resize(image, (224, 224))\n","\n","\t# update the data and labels lists, respectively\n","\tdata.append(image)\n","\tlabels.append(label)\n","\n","# convert the data and labels to NumPy arrays while scaling the pixel\n","# intensities to the range [0, 255]\n","data = np.array(data) / 255.0\n","labels = np.array(labels)\n","\n","# perform one-hot encoding on the labels\n","lb = LabelBinarizer()\n","labels = lb.fit_transform(labels)\n","labels = to_categorical(labels)\n","\n","# partition the data into training and testing splits using 80% of\n","# the data for training and the remaining 20% for testing\n","(trainX, testX, trainY, testY) = train_test_split(data, labels,\n","\ttest_size=0.20, stratify=labels, random_state=42)\n","\n","# initialize the training data augmentation object\n","trainAug = ImageDataGenerator(\n","\trotation_range=15,\n","\tfill_mode=\"nearest\")\n","\n","# load the VGG16 network, ensuring the head FC layer sets are left\n","# off\n","baseModel = VGG16(weights=\"imagenet\", include_top=False,\n","\tinput_tensor=Input(shape=(224, 224, 3)))\n","\n","# construct the head of the model that will be placed on top of the\n","# the base model\n","headModel = baseModel.output\n","headModel = AveragePooling2D(pool_size=(4, 4))(headModel)\n","headModel = Flatten(name=\"flatten\")(headModel)\n","headModel = Dense(64, activation=\"relu\")(headModel)\n","headModel = Dropout(0.5)(headModel)\n","headModel = Dense(2, activation=\"softmax\")(headModel)\n","\n","# place the head FC model on top of the base model (this will become\n","# the actual model we will train)\n","model = Model(inputs=baseModel.input, outputs=headModel)\n","\n","# loop over all layers in the base model and freeze them so they will\n","# *not* be updated during the first training process\n","for layer in baseModel.layers:\n","\tlayer.trainable = False\n","\n","# compile our model\n","print(\"[INFO] compiling model...\")\n","opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)\n","model.compile(loss=\"binary_crossentropy\", optimizer=opt,\n","\tmetrics=[\"accuracy\"])\n","\n","# train the head of the network\n","print(\"[INFO] training head...\")\n","H = model.fit_generator(\n","\ttrainAug.flow(trainX, trainY, batch_size=BS),\n","\tsteps_per_epoch=len(trainX) // BS,\n","\tvalidation_data=(testX, testY),\n","\tvalidation_steps=len(testX) // BS,\n","\tepochs=EPOCHS)\n","\n","# make predictions on the testing set\n","print(\"[INFO] evaluating network...\")\n","predIdxs = model.predict(testX, batch_size=BS)\n","\n","# for each image in the testing set we need to find the index of the\n","# label with corresponding largest predicted probability\n","predIdxs = np.argmax(predIdxs, axis=1)\n","\n","# show a nicely formatted classification report\n","print(classification_report(testY.argmax(axis=1), predIdxs,\n","\ttarget_names=lb.classes_))\n","\n","# compute the confusion matrix and and use it to derive the raw\n","# accuracy, sensitivity, and specificity\n","cm = confusion_matrix(testY.argmax(axis=1), predIdxs)\n","total = sum(sum(cm))\n","acc = (cm[0, 0] + cm[1, 1]) / total\n","sensitivity = cm[0, 0] / (cm[0, 0] + cm[0, 1])\n","specificity = cm[1, 1] / (cm[1, 0] + cm[1, 1])\n","\n","# show the confusion matrix, accuracy, sensitivity, and specificity\n","print(cm)\n","print(\"acc: {:.4f}\".format(acc))\n","print(\"sensitivity: {:.4f}\".format(sensitivity))\n","print(\"specificity: {:.4f}\".format(specificity))\n","\n","# plot the training loss and accuracy\n","N = EPOCHS\n","plt.style.use(\"ggplot\")\n","plt.figure()\n","plt.plot(np.arange(0, N), H.history[\"loss\"], label=\"train_loss\")\n","plt.plot(np.arange(0, N), H.history[\"val_loss\"], label=\"val_loss\")\n","plt.plot(np.arange(0, N), H.history[\"accuracy\"], label=\"train_acc\")\n","plt.plot(np.arange(0, N), H.history[\"val_accuracy\"], label=\"val_acc\")\n","plt.title(\"Training Loss and Accuracy on COVID-19 Dataset\")\n","plt.xlabel(\"Epoch #\")\n","plt.ylabel(\"Loss/Accuracy\")\n","plt.legend(loc=\"lower left\")\n","plt.savefig(\"plot.png\")\n","\n","# serialize the model to disk\n","print(\"[INFO] saving COVID-19 detector model...\")\n","model.save(\"covid19.model\", save_format=\"h5\")"],"execution_count":4,"outputs":[{"output_type":"stream","text":["[INFO] loading images...\n","Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n","58892288/58889256 [==============================] - 0s 0us/step\n","[INFO] compiling model...\n","[INFO] training head...\n","WARNING:tensorflow:From <ipython-input-4-f1b57c46d983>:120: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Please use Model.fit, which supports generators.\n","Epoch 1/25\n","5/5 [==============================] - 22s 4s/step - loss: 0.7297 - accuracy: 0.4500 - val_loss: 0.6427 - val_accuracy: 0.8000\n","Epoch 2/25\n","5/5 [==============================] - 21s 4s/step - loss: 0.7020 - accuracy: 0.5000 - val_loss: 0.6260 - val_accuracy: 0.6000\n","Epoch 3/25\n","5/5 [==============================] - 21s 4s/step - loss: 0.6396 - accuracy: 0.6000 - val_loss: 0.6005 - val_accuracy: 0.9000\n","Epoch 4/25\n","5/5 [==============================] - 21s 4s/step - loss: 0.5866 - accuracy: 0.7500 - val_loss: 0.5849 - val_accuracy: 1.0000\n","Epoch 5/25\n","5/5 [==============================] - 21s 4s/step - loss: 0.6879 - accuracy: 0.6000 - val_loss: 0.5733 - val_accuracy: 1.0000\n","Epoch 6/25\n","5/5 [==============================] - 21s 4s/step - loss: 0.6691 - accuracy: 0.5500 - val_loss: 0.5678 - val_accuracy: 0.8000\n","Epoch 7/25\n","5/5 [==============================] - 21s 4s/step - loss: 0.6877 - accuracy: 0.5500 - val_loss: 0.5614 - val_accuracy: 0.8000\n","Epoch 8/25\n","5/5 [==============================] - 21s 4s/step - loss: 0.5683 - accuracy: 0.7250 - val_loss: 0.5572 - val_accuracy: 0.8000\n","Epoch 9/25\n","5/5 [==============================] - 21s 4s/step - loss: 0.5645 - accuracy: 0.7500 - val_loss: 0.5449 - val_accuracy: 0.9000\n","Epoch 10/25\n","5/5 [==============================] - 22s 4s/step - loss: 0.5864 - accuracy: 0.7250 - val_loss: 0.5294 - val_accuracy: 0.9000\n","Epoch 11/25\n","5/5 [==============================] - 21s 4s/step - loss: 0.4806 - accuracy: 0.8500 - val_loss: 0.5211 - val_accuracy: 0.9000\n","Epoch 12/25\n","5/5 [==============================] - 21s 4s/step - loss: 0.4948 - accuracy: 0.8250 - val_loss: 0.5136 - val_accuracy: 0.9000\n","Epoch 13/25\n","5/5 [==============================] - 21s 4s/step - loss: 0.4989 - accuracy: 0.7750 - val_loss: 0.5021 - val_accuracy: 0.9000\n","Epoch 14/25\n","5/5 [==============================] - 21s 4s/step - loss: 0.5020 - accuracy: 0.8250 - val_loss: 0.4922 - val_accuracy: 0.9000\n","Epoch 15/25\n","5/5 [==============================] - 21s 4s/step - loss: 0.4690 - accuracy: 0.8250 - val_loss: 0.4790 - val_accuracy: 0.8000\n","Epoch 16/25\n","5/5 [==============================] - 21s 4s/step - loss: 0.4298 - accuracy: 0.9250 - val_loss: 0.4677 - val_accuracy: 0.8000\n","Epoch 17/25\n","5/5 [==============================] - 21s 4s/step - loss: 0.4868 - accuracy: 0.8500 - val_loss: 0.4619 - val_accuracy: 0.9000\n","Epoch 18/25\n","5/5 [==============================] - 21s 4s/step - loss: 0.4675 - accuracy: 0.8250 - val_loss: 0.4566 - val_accuracy: 0.9000\n","Epoch 19/25\n","5/5 [==============================] - 21s 4s/step - loss: 0.4131 - accuracy: 0.8750 - val_loss: 0.4444 - val_accuracy: 0.8000\n","Epoch 20/25\n","5/5 [==============================] - 21s 4s/step - loss: 0.4143 - accuracy: 0.8500 - val_loss: 0.4429 - val_accuracy: 0.9000\n","Epoch 21/25\n","5/5 [==============================] - 22s 4s/step - loss: 0.3627 - accuracy: 0.9500 - val_loss: 0.4352 - val_accuracy: 0.9000\n","Epoch 22/25\n","5/5 [==============================] - 21s 4s/step - loss: 0.4503 - accuracy: 0.8250 - val_loss: 0.4303 - val_accuracy: 0.9000\n","Epoch 23/25\n","5/5 [==============================] - 21s 4s/step - loss: 0.3644 - accuracy: 0.8750 - val_loss: 0.4224 - val_accuracy: 0.9000\n","Epoch 24/25\n","5/5 [==============================] - 21s 4s/step - loss: 0.4108 - accuracy: 0.9000 - val_loss: 0.4065 - val_accuracy: 0.9000\n","Epoch 25/25\n","5/5 [==============================] - 21s 4s/step - loss: 0.3615 - accuracy: 0.8500 - val_loss: 0.3839 - val_accuracy: 0.9000\n","[INFO] evaluating network...\n","              precision    recall  f1-score   support\n","\n","       covid       1.00      0.80      0.89         5\n","      normal       0.83      1.00      0.91         5\n","\n","    accuracy                           0.90        10\n","   macro avg       0.92      0.90      0.90        10\n","weighted avg       0.92      0.90      0.90        10\n","\n","[[4 1]\n"," [0 5]]\n","acc: 0.9000\n","sensitivity: 0.8000\n","specificity: 1.0000\n","[INFO] saving COVID-19 detector model...\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]}]}