Breast Cancer Detection - Paper Purpose.ipynb

{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np \nimport pandas as pd \nfrom collections import Counter\nimport os\nprint(os.listdir(\"../input\"))\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%reload_ext autoreload\n%autoreload 2\n%matplotlib inline","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from fastai.vision import *\nfrom fastai.metrics import *\n\nimport numpy as np\nnp.random.seed(7)\n\nimport torch\ntorch.cuda.manual_seed_all(7)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"tfms = get_transforms(do_flip=True, flip_vert=True, max_rotate=20., max_zoom=0.05)\nlen(tfms)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"path = '../input/IDC_regular_ps50_idx5/'\ndata = ImageDataBunch.from_folder(path, ds_tfms=tfms, valid_pct=0.2,\n                                  size=48, bs=128).normalize(imagenet_stats)\n\ndata.show_batch()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"0 indicates `no IDC` (no breast cancer) while 1 indicates `IDC`  (breast cancer) \n\nIDC -> Invasive Ductal Carcinoma"},{"metadata":{},"cell_type":"markdown","source":"## Distribution of the classes in the training and validation splits"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_counts = Counter(data.train_ds.y)\ntrain_counts.most_common()","execution_count":21,"outputs":[{"output_type":"execute_result","execution_count":21,"data":{"text/plain":"[(Category 0, 159089), (Category 1, 62931)]"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"valid_counts = Counter(data.valid_ds.y)\nvalid_counts.most_common()","execution_count":22,"outputs":[{"output_type":"execute_result","execution_count":22,"data":{"text/plain":"[(Category 0, 39649), (Category 1, 15855)]"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.label_list","execution_count":23,"outputs":[{"output_type":"execute_result","execution_count":23,"data":{"text/plain":"LabelLists;\n\nTrain: LabelList (222020 items)\nx: ImageList\nImage (3, 48, 48),Image (3, 48, 48),Image (3, 48, 48),Image (3, 48, 48),Image (3, 48, 48)\ny: CategoryList\n1,1,1,1,1\nPath: ../input/IDC_regular_ps50_idx5;\n\nValid: LabelList (55504 items)\nx: ImageList\nImage (3, 48, 48),Image (3, 48, 48),Image (3, 48, 48),Image (3, 48, 48),Image (3, 48, 48)\ny: CategoryList\n0,1,0,0,0\nPath: ../input/IDC_regular_ps50_idx5;\n\nTest: None"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Looking at a few samples "},{"metadata":{"trusted":true},"cell_type":"code","source":"print(data.train_ds[0]);\nshow_image(data.train_ds[0][0], figsize=(3,3))","execution_count":24,"outputs":[{"output_type":"stream","text":"(Image (3, 48, 48), Category 1)\n","name":"stdout"},{"output_type":"execute_result","execution_count":24,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f442886e7f0>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 216x216 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(data.train_ds[5]);\nshow_image(data.train_ds[5][0],)","execution_count":25,"outputs":[{"output_type":"stream","text":"(Image (3, 48, 48), Category 1)\n","name":"stdout"},{"output_type":"execute_result","execution_count":25,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f4408071320>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 216x216 with 1 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(data.train_ds[45]);\nshow_image(data.train_ds[45][0])","execution_count":26,"outputs":[{"output_type":"stream","text":"(Image (3, 48, 48), Category 0)\n","name":"stdout"},{"output_type":"execute_result","execution_count":26,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f43af5d8518>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 216x216 with 1 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(data.valid_ds[45]);\nshow_image(data.valid_ds[45][0])","execution_count":27,"outputs":[{"output_type":"stream","text":"(Image (3, 48, 48), Category 0)\n","name":"stdout"},{"output_type":"execute_result","execution_count":27,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f4428b797b8>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 216x216 with 1 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(data.valid_ds[53]);\nshow_image(data.valid_ds[53][0])","execution_count":28,"outputs":[{"output_type":"stream","text":"(Image (3, 48, 48), Category 0)\n","name":"stdout"},{"output_type":"execute_result","execution_count":28,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f4438fb60f0>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 216x216 with 1 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(data.valid_ds[105]);\nshow_image(data.valid_ds[105][0])","execution_count":29,"outputs":[{"output_type":"stream","text":"(Image (3, 48, 48), Category 0)\n","name":"stdout"},{"output_type":"execute_result","execution_count":29,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f443a40d9e8>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 216x216 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(data.valid_ds[598]);\nshow_image(data.valid_ds[598][0])","execution_count":30,"outputs":[{"output_type":"stream","text":"(Image (3, 48, 48), Category 0)\n","name":"stdout"},{"output_type":"execute_result","execution_count":30,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f443a446ac8>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 216x216 with 1 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(data.valid_ds[928]);\nshow_image(data.valid_ds[928][0])","execution_count":31,"outputs":[{"output_type":"stream","text":"(Image (3, 48, 48), Category 1)\n","name":"stdout"},{"output_type":"execute_result","execution_count":31,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f443a3c5b70>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 216x216 with 1 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from torch import nn\n\nweights = [0.4, 1]\nclass_weights=torch.FloatTensor(weights).cuda()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Begin the training\nlearn = cnn_learner(data, models.resnet50, metrics=[accuracy], model_dir='/tmp/models').to_fp16()\nlearn.loss_func = nn.CrossEntropyLoss(weight=class_weights)\nlearn.lr_find()\nlearn.recorder.plot()\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn.fit_one_cycle(5, max_lr=slice(1e-04, 1e-05));\nlearn.recorder.plot_losses()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"del learn","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn = cnn_learner(data, models.resnet50, metrics=[accuracy], model_dir='/tmp/models').to_fp16()\nlearn.loss_func = nn.CrossEntropyLoss(weight=class_weights)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn.fit_one_cycle(5, max_lr=slice(1e-03, 1e-04));\nlearn.recorder.plot_losses()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"interp = ClassificationInterpretation.from_learner(learn)\ninterp.plot_confusion_matrix(figsize=(5,5))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.metrics import classification_report\n\ndef return_classification_report(learn):\n    \n    ground_truth = []\n    pred_labels = []\n\n    for i in range(len(learn.data.valid_ds)):\n        temp_pred = str(learn.predict(learn.data.valid_ds[i][0])[0])\n        temp_truth = str(learn.data.valid_ds[i]).split('), ', 1)[1].replace('Category ', '').replace(')', '')\n        pred_labels.append(temp_pred)\n        ground_truth.append(temp_truth)\n\n    assert len(pred_labels) == len(ground_truth)\n    \n    return classification_report(ground_truth, pred_labels, target_names=data.classes)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(return_classification_report(learn))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn.unfreeze()\nlearn.fit_one_cycle(2, max_lr = slice(1e-03))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"interp = ClassificationInterpretation.from_learner(learn)\ninterp.plot_confusion_matrix(figsize=(5,5))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(return_classification_report(learn))","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}