skin-cancer-identification.ipynb

{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current 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gnant-vs-benign/train/benign/688.jpg\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np\nimport tensorflow as tf \nimport matplotlib.pyplot as plt\nimport random\nimport seaborn as sns\nfrom keras.models import Sequential\nfrom keras.layers import Conv2D,Dense,Flatten,Dropout,AveragePooling2D\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.callbacks import ReduceLROnPlateau","execution_count":2,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_ben = \"../input/skin-cancer-malignant-vs-benign/train/benign\"\ntrain_mal = \"../input/skin-cancer-malignant-vs-benign/train/malignant\"","execution_count":3,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = plt.figure()\nax = fig.add_axes([0,0,1,1])\nlangs = ['normal', 'danger']\nstudents = [len(train_ben),len(train_mal)]\nax.bar(langs,students)\nplt.show()","execution_count":4,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"training_dir = \"../input/skin-cancer-malignant-vs-benign/train\"\ntraining_generator = ImageDataGenerator(rescale=1./255,\n                                   rotation_range=15,\n                                   shear_range=0.2,\n                                   zoom_range=0.2 )\ntrain_generator = training_generator.flow_from_directory(training_dir,target_size=(200,200),batch_size=4,class_mode='binary')","execution_count":7,"outputs":[{"output_type":"stream","text":"Found 2637 images belonging to 2 classes.\n","name":"stdout"}]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"testing_dir = \"../input/skin-cancer-malignant-vs-benign/test\"\ntesting_generator = ImageDataGenerator(rescale=1./255)\ntest_generator = testing_generator.flow_from_directory(testing_dir,target_size=(200,200),batch_size=4,class_mode='binary')","execution_count":8,"outputs":[{"output_type":"stream","text":"Found 660 images belonging to 2 classes.\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nak=tf.keras.Sequential([\n    tf.keras.layers.Conv2D(32,(3,3),input_shape=(200,200,3),activation='relu'),\n    tf.keras.layers.MaxPooling2D(2,2),\n    \n    tf.keras.layers.Conv2D(64,(3,3),activation='relu'),\n    tf.keras.layers.MaxPooling2D(2,2),\n    tf.keras.layers.Dropout(0.2),\n    \n    tf.keras.layers.Conv2D(128,(3,3),activation='relu'),\n    tf.keras.layers.MaxPooling2D(2,2),\n    tf.keras.layers.Dropout(0.2),\n\n    \n    tf.keras.layers.Flatten(),\n    tf.keras.layers.Dropout(0.2),\n    tf.keras.layers.Dense(128,activation='relu'),\n    tf.keras.layers.Dense(1,activation='sigmoid')\n    \n])\nak.summary()","execution_count":13,"outputs":[{"output_type":"stream","text":"Model: \"sequential_1\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nconv2d_4 (Conv2D)            (None, 198, 198, 32)      896       \n_________________________________________________________________\nmax_pooling2d_4 (MaxPooling2 (None, 99, 99, 32)        0         \n_________________________________________________________________\nconv2d_5 (Conv2D)            (None, 97, 97, 64)        18496     \n_________________________________________________________________\nmax_pooling2d_5 (MaxPooling2 (None, 48, 48, 64)        0         \n_________________________________________________________________\ndropout_3 (Dropout)          (None, 48, 48, 64)        0         \n_________________________________________________________________\nconv2d_6 (Conv2D)            (None, 46, 46, 128)       73856     \n_________________________________________________________________\nmax_pooling2d_6 (MaxPooling2 (None, 23, 23, 128)       0         \n_________________________________________________________________\ndropout_4 (Dropout)          (None, 23, 23, 128)       0         \n_________________________________________________________________\nflatten_1 (Flatten)          (None, 67712)             0         \n_________________________________________________________________\ndropout_5 (Dropout)          (None, 67712)             0         \n_________________________________________________________________\ndense_2 (Dense)              (None, 128)               8667264   \n_________________________________________________________________\ndense_3 (Dense)              (None, 1)                 129       \n=================================================================\nTotal params: 8,760,641\nTrainable params: 8,760,641\nNon-trainable params: 0\n_________________________________________________________________\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"ak.compile(optimizer = tf.keras.optimizers.Adam(lr=0.001), loss = 'binary_crossentropy', metrics = ['accuracy'])","execution_count":14,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history = ak.fit_generator(train_generator,\n                           validation_data = test_generator,\n                           epochs = 5,\n                           verbose = 1)","execution_count":15,"outputs":[{"output_type":"stream","text":"Epoch 1/5\n660/660 [==============================] - 136s 206ms/step - loss: 0.6755 - accuracy: 0.6045 - val_loss: 0.5817 - val_accuracy: 0.7273\nEpoch 2/5\n660/660 [==============================] - 135s 205ms/step - loss: 0.5620 - accuracy: 0.7171 - val_loss: 0.5471 - val_accuracy: 0.7379\nEpoch 3/5\n660/660 [==============================] - 134s 203ms/step - loss: 0.4820 - accuracy: 0.7626 - val_loss: 0.4339 - val_accuracy: 0.7727\nEpoch 4/5\n660/660 [==============================] - 134s 203ms/step - loss: 0.4594 - accuracy: 0.7751 - val_loss: 0.3844 - val_accuracy: 0.8045\nEpoch 5/5\n660/660 [==============================] - 134s 204ms/step - loss: 0.4303 - accuracy: 0.7937 - val_loss: 0.4194 - val_accuracy: 0.7955\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"%matplotlib inline\naccuracy = history.history['accuracy']\nval_accuracy = history.history['val_accuracy']\nloss = history.history['loss']\nval_loss = history.history['val_loss']\n\nepochs = range(len(accuracy))\n\nplt.plot(epochs, accuracy, 'r', label='Training accuracy')\nplt.plot(epochs, val_accuracy, 'b', label='Validation accuracy')\nplt.plot(epochs, loss, 'g', label='loss')\nplt.plot(epochs, val_loss, 'y', label='Validation loss')\nplt.title('Training,validation accuracy and loss')\nplt.legend(loc=0)\nplt.figure()","execution_count":16,"outputs":[{"output_type":"execute_result","execution_count":16,"data":{"text/plain":"<Figure size 432x288 with 0 Axes>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 0 Axes>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Loss of the model is - \" , ak.evaluate(test_generator)[0]*100 , \"%\")\nprint(\"Accuracy of the model is - \" , ak.evaluate(test_generator)[1]*100 , \"%\")","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4}