Covid-19.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Covid-19.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"sjhR84QlmXtO","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":121},"executionInfo":{"status":"ok","timestamp":1593330949411,"user_tz":-330,"elapsed":16143,"user":{"displayName":"Jeevan Kishore","photoUrl":"","userId":"15092610011952393479"}},"outputId":"62d951b6-4edd-46e5-bea5-060cc2cc9e4e"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"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/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"kE8e9X04mYWc","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1593330952186,"user_tz":-330,"elapsed":1279,"user":{"displayName":"Jeevan Kishore","photoUrl":"","userId":"15092610011952393479"}}},"source":["import numpy as np\n","import pandas as pd"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"1R5LDE8Jmkjw","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1593330954250,"user_tz":-330,"elapsed":3094,"user":{"displayName":"Jeevan Kishore","photoUrl":"","userId":"15092610011952393479"}},"outputId":"3c5c0544-c718-4198-dde7-55daa337999c"},"source":["import keras"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Using TensorFlow backend.\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"XQ_MrQt7ml2I","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1593330954252,"user_tz":-330,"elapsed":2786,"user":{"displayName":"Jeevan Kishore","photoUrl":"","userId":"15092610011952393479"}}},"source":["from keras.layers import Conv2D,Dropout,Flatten,Dense,MaxPooling2D\n","from keras.models import Sequential\n","import tensorflow as tf"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"K3G7QIpKmxKH","colab_type":"code","colab":{}},"source":["\n","model = Sequential()\n","model.add(Conv2D(32,kernel_size=(3,3),input_shape=(224,224,3),activation='relu'))\n","model.add(Conv2D(64,kernel_size=(3,3),activation='relu'))\n","model.add(MaxPooling2D(pool_size=(2,2)))\n","model.add(Conv2D(128,kernel_size=(3,3),activation='relu'))\n","model.add(Conv2D(128,kernel_size=(3,3),activation='relu'))\n","model.add(MaxPooling2D(pool_size=(2,2)))\n","model.add(Dropout(rate=0.25))\n","model.add(Conv2D(256,kernel_size=(3,3),activation='relu'))\n","model.add(Dropout(rate=0.25))\n","model.add(Conv2D(256,kernel_size=(3,3),activation='relu'))\n","model.add(MaxPooling2D(pool_size=(2,2)))\n","model.add(Dropout(rate=0.25))\n","model.add(Flatten())\n","model.add(Dense(128,activation='relu'))\n","model.add(Dropout(rate=0.5))\n","model.add(Dense(1,activation='sigmoid'))\n","model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"RAwKlVDspfln","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":756},"executionInfo":{"status":"ok","timestamp":1592993223120,"user_tz":-330,"elapsed":783,"user":{"displayName":"Jeevan Kishore","photoUrl":"","userId":"01612073368942994583"}},"outputId":"41bcdb7d-e278-4d2b-ad7d-feb3b4216991"},"source":["model.summary()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Model: \"sequential_2\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","conv2d_7 (Conv2D)            (None, 222, 222, 32)      896       \n","_________________________________________________________________\n","conv2d_8 (Conv2D)            (None, 220, 220, 64)      18496     \n","_________________________________________________________________\n","max_pooling2d_4 (MaxPooling2 (None, 110, 110, 64)      0         \n","_________________________________________________________________\n","conv2d_9 (Conv2D)            (None, 108, 108, 128)     73856     \n","_________________________________________________________________\n","conv2d_10 (Conv2D)           (None, 106, 106, 128)     147584    \n","_________________________________________________________________\n","max_pooling2d_5 (MaxPooling2 (None, 53, 53, 128)       0         \n","_________________________________________________________________\n","dropout_6 (Dropout)          (None, 53, 53, 128)       0         \n","_________________________________________________________________\n","conv2d_11 (Conv2D)           (None, 51, 51, 256)       295168    \n","_________________________________________________________________\n","dropout_7 (Dropout)          (None, 51, 51, 256)       0         \n","_________________________________________________________________\n","conv2d_12 (Conv2D)           (None, 49, 49, 256)       590080    \n","_________________________________________________________________\n","max_pooling2d_6 (MaxPooling2 (None, 24, 24, 256)       0         \n","_________________________________________________________________\n","dropout_8 (Dropout)          (None, 24, 24, 256)       0         \n","_________________________________________________________________\n","flatten_2 (Flatten)          (None, 147456)            0         \n","_________________________________________________________________\n","dense_4 (Dense)              (None, 128)               18874496  \n","_________________________________________________________________\n","dropout_9 (Dropout)          (None, 128)               0         \n","_________________________________________________________________\n","dense_5 (Dense)              (None, 128)               16512     \n","_________________________________________________________________\n","dropout_10 (Dropout)         (None, 128)               0         \n","_________________________________________________________________\n","dense_6 (Dense)              (None, 1)                 129       \n","=================================================================\n","Total params: 20,017,217\n","Trainable params: 20,017,217\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"mMiNKmLdqG98","colab_type":"code","colab":{}},"source":["from keras.callbacks import ModelCheckpoint,CSVLogger,EarlyStopping"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Nt5dQ98lqRzb","colab_type":"code","colab":{}},"source":["cp = ModelCheckpoint('/content/drive/My Drive/Covid-19 CT Scans/{epoch:02d}-{val_accuracy:.2f}.h5',monitor='val_accuracy',mode='max',save_best_only=True)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"sdPL8sZRq8_C","colab_type":"code","colab":{}},"source":["csv = CSVLogger('performance.csv')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"cmWwgxXnrIax","colab_type":"code","colab":{}},"source":["es = EarlyStopping(monitor='val_loss',patience=3,mode='min')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"GxDRpDtErO7j","colab_type":"code","colab":{}},"source":["callbacks= [csv,es,cp]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"3Iy1BnY6rSI3","colab_type":"code","colab":{}},"source":["from keras.preprocessing.image import ImageDataGenerator"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"MGRxU8CDrZBr","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1593102193372,"user_tz":-330,"elapsed":901,"user":{"displayName":"Jeevan Kishore","photoUrl":"","userId":"01612073368942994583"}},"outputId":"a30ac4c3-f754-4f5e-9d29-fe61069586e7"},"source":["train_datagen = ImageDataGenerator(\n","    rescale=1./255,\n","    shear_range = 0.2,\n","    zoom_range = 0.2,\n","    horizontal_flip = True \n",")\n","test_datagen = ImageDataGenerator(rescale=1./255)\n","train_generator = train_datagen.flow_from_directory(\n","    '/content/drive/My Drive/Covid-19 CT Scans/xray_dataset_covid19/train',\n","    target_size=(224,224),\n","    batch_size=32,\n","    class_mode='binary'\n",")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Found 185 images belonging to 2 classes.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"15EVqKRCsS2x","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1593102195533,"user_tz":-330,"elapsed":887,"user":{"displayName":"Jeevan Kishore","photoUrl":"","userId":"01612073368942994583"}},"outputId":"e961020c-25c9-4bfe-c226-8fa8f38661f3"},"source":["validation_generator = test_datagen.flow_from_directory(\n","   '/content/drive/My Drive/Covid-19 CT Scans/xray_dataset_covid19/test',\n","   target_size=(224,224),\n","   batch_size=32,\n","    class_mode='binary'\n",")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Found 50 images belonging to 2 classes.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"ikW5RdmIcsxl","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1593102204466,"user_tz":-330,"elapsed":982,"user":{"displayName":"Jeevan Kishore","photoUrl":"","userId":"01612073368942994583"}},"outputId":"6691c420-dbd0-4ee0-9fa7-613e767219d4"},"source":["import tensorflow as tf \n","tf.test.gpu_device_name() \n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'/device:GPU:0'"]},"metadata":{"tags":[]},"execution_count":51}]},{"cell_type":"code","metadata":{"id":"UtuTjqeMs-Zo","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":187},"executionInfo":{"status":"ok","timestamp":1593103823724,"user_tz":-330,"elapsed":1605359,"user":{"displayName":"Jeevan Kishore","photoUrl":"","userId":"01612073368942994583"}},"outputId":"825433bf-ae74-4225-c570-ba823f2f8d0f"},"source":["history = model.fit_generator(train_generator,steps_per_epoch=185,callbacks=callbacks,epochs=15,validation_data=validation_generator,validation_steps=50)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Epoch 1/15\n","185/185 [==============================] - 365s 2s/step - loss: 0.2556 - accuracy: 0.8935 - val_loss: 0.1132 - val_accuracy: 0.9800\n","Epoch 2/15\n","185/185 [==============================] - 310s 2s/step - loss: 0.1057 - accuracy: 0.9646 - val_loss: 0.0295 - val_accuracy: 0.9800\n","Epoch 3/15\n","185/185 [==============================] - 308s 2s/step - loss: 0.0686 - accuracy: 0.9751 - val_loss: 0.1708 - val_accuracy: 0.9800\n","Epoch 4/15\n","185/185 [==============================] - 307s 2s/step - loss: 0.0658 - accuracy: 0.9751 - val_loss: 0.1035 - val_accuracy: 0.9600\n","Epoch 5/15\n","185/185 [==============================] - 308s 2s/step - loss: 0.0666 - accuracy: 0.9755 - val_loss: 0.0551 - val_accuracy: 1.0000\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"snm0yATatms0","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":289},"executionInfo":{"status":"ok","timestamp":1593103899975,"user_tz":-330,"elapsed":1085,"user":{"displayName":"Jeevan Kishore","photoUrl":"","userId":"01612073368942994583"}},"outputId":"39dbca0f-ba5a-4502-ecab-c2e2bb16a4e7"},"source":["history.history"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'accuracy': [0.8935201, 0.96458006, 0.9751008, 0.9751008, 0.9754515],\n"," 'loss': [0.25573066447505927,\n","  0.1045667632992781,\n","  0.06862022685513812,\n","  0.0661085041235326,\n","  0.06717499330884469],\n"," 'val_accuracy': [0.9800000190734863,\n","  0.9800000190734863,\n","  0.9800000190734863,\n","  0.9599999785423279,\n","  1.0],\n"," 'val_loss': [0.11317411810159683,\n","  0.029534390196204185,\n","  0.17081865668296814,\n","  0.10352620482444763,\n","  0.055149298161268234]}"]},"metadata":{"tags":[]},"execution_count":53}]},{"cell_type":"code","metadata":{"id":"8nwcUhGkpyt2","colab_type":"code","colab":{}},"source":["import matplotlib.pyplot as plt\n","%matplotlib inline"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"cBC1fd-1rBcb","colab_type":"code","colab":{}},"source":["xc = range(5)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"R0XnhEr0p8Dl","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":405},"executionInfo":{"status":"ok","timestamp":1593103935730,"user_tz":-330,"elapsed":1171,"user":{"displayName":"Jeevan Kishore","photoUrl":"","userId":"01612073368942994583"}},"outputId":"48a3a069-abfe-4c52-dac8-d42a7cad78f0"},"source":["plt.figure(figsize=(10,6))\n","plt.plot(xc,history.history['val_loss'])\n","plt.plot(xc,history.history['loss'])\n","plt.xlabel('Epochs')\n","plt.ylabel('Loss')\n","labels=['Val_loss','Train_loss']\n","plt.legend(labels,loc='best')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x7fddb8afd5c0>"]},"metadata":{"tags":[]},"execution_count":57},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x432 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"ZqTsy8FrqHpk","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":405},"executionInfo":{"status":"ok","timestamp":1593103943353,"user_tz":-330,"elapsed":1432,"user":{"displayName":"Jeevan Kishore","photoUrl":"","userId":"01612073368942994583"}},"outputId":"cc31fec0-9058-4efc-df48-21dc11e00d98"},"source":["plt.figure(figsize=(10,6))\n","plt.plot(xc,history.history['accuracy'])\n","plt.plot(xc,history.history['val_accuracy'])\n","plt.xlabel('Epochs')\n","plt.ylabel('Accuracy')\n","label=['Train_accuracy','Val_accuracy']\n","plt.legend(label,loc='best')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x7fddb8b4b4a8>"]},"metadata":{"tags":[]},"execution_count":58},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x432 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"d1pfX7vbszd7","colab_type":"code","colab":{}},"source":["model.save('covid-19_detection.h5')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Bms0_0B4s-rI","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1593103955462,"user_tz":-330,"elapsed":2428,"user":{"displayName":"Jeevan Kishore","photoUrl":"","userId":"01612073368942994583"}},"outputId":"27466038-b44c-4dce-8063-973558aaf438"},"source":["model.evaluate_generator(validation_generator)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[0.048178259283304214, 1.0]"]},"metadata":{"tags":[]},"execution_count":59}]},{"cell_type":"code","metadata":{"id":"mrWaeNXqEybg","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1593104076396,"user_tz":-330,"elapsed":951,"user":{"displayName":"Jeevan Kishore","photoUrl":"","userId":"01612073368942994583"}},"outputId":"3bf466b0-69dc-4c06-c97b-5629d54b9624"},"source":["validation_generator.class_indices"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'COVID-19': 0, 'NORMAL': 1}"]},"metadata":{"tags":[]},"execution_count":67}]},{"cell_type":"code","metadata":{"id":"LVzfgwvmTigw","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1593331001895,"user_tz":-330,"elapsed":24247,"user":{"displayName":"Jeevan Kishore","photoUrl":"","userId":"15092610011952393479"}}},"source":["model1 = keras.models.load_model('/content/drive/My Drive/Covid-19 CT Scans/covid-19_detection.h5')"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"id":"GHinZQuk0qTu","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1593331078182,"user_tz":-330,"elapsed":600,"user":{"displayName":"Jeevan Kishore","photoUrl":"","userId":"15092610011952393479"}}},"source":["def predict_image(path):\n","  image = tf.keras.preprocessing.image.load_img(path,target_size=(224,224))\n","  input_arr = keras.preprocessing.image.img_to_array(image)\n","  input_arr = np.expand_dims(input_arr,axis=0)  # Convert single image to a batch.\n","  preds = model1.predict(input_arr,verbose=0)\n","  if preds[0][0] == 0: outcome='Covid-19'\n","  else : outcome = 'Normal'\n","  print(outcome)"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"id":"7MRlxr7CXB_3","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1593331086207,"user_tz":-330,"elapsed":7191,"user":{"displayName":"Jeevan Kishore","photoUrl":"","userId":"15092610011952393479"}},"outputId":"4099f8d6-e3db-4e45-bbd0-81d5d5f97e40"},"source":["predict_image('/content/drive/My Drive/Covid-19 CT Scans/5.jpg')"],"execution_count":10,"outputs":[{"output_type":"stream","text":["Covid-19\n"],"name":"stdout"}]}]}