{"cells":[{"metadata":{},"cell_type":"markdown","source":"# Intro\nWelcome to the [VinBigData Chest X-ray Abnormalities Detection](https://www.kaggle.com/c/vinbigdata-chest-xray-abnormalities-detection/data) compedition.\n\n\n\nWe consider 14 critical radiographic findings as listed below (click for further informations):\n\n0 - [Aortic enlargement](https://en.wikipedia.org/wiki/Aortic_aneurysm)
\n1 - [Atelectasis](https://en.wikipedia.org/wiki/Atelectasis)
\n2 - [Calcification](https://en.wikipedia.org/wiki/Calcification)
\n3 - [Cardiomegaly](https://en.wikipedia.org/wiki/Cardiomegaly)
\n4 - [Consolidation](https://en.wikipedia.org/wiki/Pulmonary_consolidation)
\n5 - [ILD](https://en.wikipedia.org/wiki/Interstitial_lung_disease)
\n6 - [Infiltration](https://en.wikipedia.org/wiki/Infiltration_(medical))
\n7 - [Lung Opacity](https://en.wikipedia.org/wiki/Ground-glass_opacity)
\n8 - [Nodule/Mass](https://en.wikipedia.org/wiki/Lung_nodule)
\n9 - Other lesion
\n10 - [Pleural effusion](https://en.wikipedia.org/wiki/Pleural_effusion)
\n11 - [Pleural thickening](https://en.wikipedia.org/wiki/Pleural_thickening)
\n12 - [Pneumothorax](https://en.wikipedia.org/wiki/Pneumothorax)
\n13 - [Pulmonary fibrosis](https://en.wikipedia.org/wiki/Pulmonary_fibrosis#:~:text=Pulmonary%20fibrosis%20is%20a%20condition,%2C%20pneumothorax%2C%20and%20lung%20cancer.)\n\nPlease vote the notebook up if it helps you. Feel free to leave a comment above the notebook. Thank you. "},{"metadata":{},"cell_type":"markdown","source":"# Libraries"},{"metadata":{"trusted":true},"cell_type":"code","source":"import os\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib\nimport pydicom as dicom\nimport cv2\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Path"},{"metadata":{"trusted":true},"cell_type":"code","source":"path = '/kaggle/input/vinbigdata-chest-xray-abnormalities-detection/'\nos.listdir(path)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Load Data"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_data = pd.read_csv(path+'train.csv')\nsamp_subm = pd.read_csv(path+'sample_submission.csv')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Overview"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"print('Number train samples:', len(train_data.index))\nprint('Number test samples:', len(samp_subm.index))","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"fig, ax = plt.subplots(1, 1, figsize=(12, 4))\nx = train_data['class_name'].value_counts().keys()\ny = train_data['class_name'].value_counts().values\nax.bar(x, y)\nax.set_xticklabels(x, rotation=90)\nax.set_title('Distribution of the labels')\nplt.grid()\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"As we can see the dataset is inbalanced."},{"metadata":{},"cell_type":"markdown","source":"# Read dicom Files"},{"metadata":{"trusted":true},"cell_type":"code","source":"idnum = 2\nimage_id = train_data.loc[idnum, 'image_id']\ndata_file = dicom.dcmread(path+'train/'+image_id+'.dicom')\nimg = data_file.pixel_array","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Print meta data of the image:"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"print(data_file)","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"print('Image shape:', img.shape)","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"bbox = [train_data.loc[idnum, 'x_min'],\n train_data.loc[idnum, 'y_min'],\n train_data.loc[idnum, 'x_max'],\n train_data.loc[idnum, 'y_max']]\nfig, ax = plt.subplots(1, 1, figsize=(20, 4))\nax.imshow(img, cmap='gray')\np = matplotlib.patches.Rectangle((bbox[0], bbox[1]),\n bbox[2]-bbox[0],\n bbox[3]-bbox[1],\n ec='r', fc='none', lw=2.)\nax.add_patch(p)\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Show Examples\nPlot 3 images of every class with the bounding boxes:"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"def plot_example(idx_list):\n fig, axs = plt.subplots(1, 3, figsize=(15, 10))\n fig.subplots_adjust(hspace = .1, wspace=.1)\n axs = axs.ravel()\n for i in range(3):\n image_id = train_data.loc[idx_list[i], 'image_id']\n data_file = dicom.dcmread(path+'train/'+image_id+'.dicom')\n img = data_file.pixel_array\n axs[i].imshow(img, cmap='gray')\n axs[i].set_title(train_data.loc[idx_list[i], 'class_name'])\n axs[i].set_xticklabels([])\n axs[i].set_yticklabels([])\n if train_data.loc[idx_list[i], 'class_name'] != 'No finding':\n bbox = [train_data.loc[idx_list[i], 'x_min'],\n train_data.loc[idx_list[i], 'y_min'],\n train_data.loc[idx_list[i], 'x_max'],\n train_data.loc[idx_list[i], 'y_max']]\n p = matplotlib.patches.Rectangle((bbox[0], bbox[1]),\n bbox[2]-bbox[0],\n bbox[3]-bbox[1],\n ec='r', fc='none', lw=2.)\n axs[i].add_patch(p)\n \nfor num in range(15):\n idx_list = train_data[train_data['class_id']==num][0:3].index.values\n plot_example(idx_list)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","collapsed":true,"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":false},"cell_type":"markdown","source":"# Write Output\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"samp_subm.to_csv('submission.csv', index=False)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}