Delete Final_Notebook.ipynb

Final_Notebook.ipynb

@@ -1,1566 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "54cd60f1",
-   "metadata": {},
-   "source": [
-    "# Importing the Required Libraries."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ccefe4f8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "env: SM_FRAMEWORK=tf.keras\n",
-      "Segmentation Models: using `tf.keras` framework.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import keras \n",
-    "%env SM_FRAMEWORK=tf.keras\n",
-    "import segmentation_models as sm\n",
-    "import glob\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy import ndimage\n",
-    "from scipy.ndimage import label, generate_binary_structure\n",
-    "import radiomics\n",
-    "import cv2\n",
-    "import SimpleITK as sitk\n",
-    "import six\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "%matplotlib inline\n",
-    "from sklearn.feature_selection import SelectKBest\n",
-    "from sklearn.feature_selection import mutual_info_classif\n",
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "from sklearn.feature_selection import RFE\n",
-    "from sklearn.metrics import roc_auc_score\n",
-    "from sklearn.feature_selection import SelectFromModel\n",
-    "from sklearn.linear_model import Lasso, LogisticRegression\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.decomposition import PCA \n",
-    "from sklearn.svm import LinearSVC\n",
-    "from sklearn.datasets import load_iris\n",
-    "from sklearn.feature_selection import SelectFromModel\n",
-    "from sklearn.feature_selection import mutual_info_classif as MIC\n",
-    "from sklearn.feature_selection import mutual_info_classif, mutual_info_regression\n",
-    "from sklearn.feature_selection import SelectKBest, SelectPercentile\n",
-    "from sklearn.feature_selection import SequentialFeatureSelector\n",
-    "from sklearn.neighbors import KNeighborsClassifier\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "from sklearn.svm import SVC\n",
-    "from sklearn.metrics import f1_score\n",
-    "from sklearn.model_selection import cross_validate\n",
-    "from sklearn.tree import DecisionTreeClassifier \n",
-    "from sklearn.linear_model import Perceptron\n",
-    "from sklearn.model_selection import RandomizedSearchCV\n",
-    "from sklearn.model_selection import GridSearchCV\n",
-    "# from hyperopt import hp\n",
-    "\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "af822604",
-   "metadata": {},
-   "source": [
-    "# Defining the path to Model and Images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "f371ad9f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "IMAGE_SIZE = (256,256,3)\n",
-    "\n",
-    "path_base_model = './/models//'\n",
-    "path_base_input = './/Normal//'\n",
-    "path_base_mask = './/Normal_Mask//'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "47a20058",
-   "metadata": {},
-   "source": [
-    "# Loading the models"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "967377df",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "BACKBONE = 'efficientnetb0'\n",
-    "model1 = sm.Unet(BACKBONE, input_shape = (IMAGE_SIZE[0],IMAGE_SIZE[1],IMAGE_SIZE[2]),classes=1, activation='sigmoid',encoder_weights='imagenet')\n",
-    "model2 = sm.Unet(BACKBONE, input_shape = (IMAGE_SIZE[0],IMAGE_SIZE[1],IMAGE_SIZE[2]),classes=1, activation='sigmoid',encoder_weights='imagenet')\n",
-    "model3 = sm.Unet(BACKBONE, input_shape = (IMAGE_SIZE[0],IMAGE_SIZE[1],IMAGE_SIZE[2]),classes=1, activation='sigmoid',encoder_weights='imagenet')\n",
-    "\n",
-    "BACKBONE = 'efficientnetb7'\n",
-    "model4 = sm.Unet(BACKBONE, input_shape = (IMAGE_SIZE[0],IMAGE_SIZE[1],IMAGE_SIZE[2]),classes=1, activation='sigmoid',encoder_weights='imagenet')\n",
-    "model5 = sm.Unet(BACKBONE, input_shape = (IMAGE_SIZE[0],IMAGE_SIZE[1],IMAGE_SIZE[2]),classes=1, activation='sigmoid',encoder_weights='imagenet')\n",
-    "\n",
-    "preprocess_input = sm.get_preprocessing(BACKBONE)\n",
-    "\n",
-    "model1.load_weights(path_base_model + 'model1.hdf5')\n",
-    "model2.load_weights(path_base_model + 'model2.hdf5')\n",
-    "model3.load_weights(path_base_model + 'model3.hdf5')\n",
-    "model4.load_weights(path_base_model + 'model4.hdf5')\n",
-    "model5.load_weights(path_base_model + 'model5.hdf5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec1f8366",
-   "metadata": {},
-   "source": [
-    "# Defining Required Functions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "3865b721",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def preprocessing_HE(img_):\n",
-    "    \n",
-    "    hist, bins = np.histogram(img_.flatten(), 256,[0,256])\n",
-    "    cdf = hist.cumsum()\n",
-    "    cdf_m = np.ma.masked_equal(cdf,0)\n",
-    "    cdf_m = (cdf_m - cdf_m.min())*255/(cdf_m.max()-cdf_m.min())\n",
-    "    cdf = np.ma.filled(cdf_m,0).astype('uint8')\n",
-    "\n",
-    "    img_2 = cdf[img_]\n",
-    "    \n",
-    "    return img_2  \n",
-    "        \n",
-    "def get_binary_mask (mask_, th_ = 0.5):\n",
-    "    mask_[mask_>th_]  = 1\n",
-    "    mask_[mask_<=th_] = 0\n",
-    "    return mask_\n",
-    "    \n",
-    "def ensemble_results (mask1_, mask2_, mask3_, mask4_, mask5_):\n",
-    "    \n",
-    "    mask1_ = get_binary_mask (mask1_)\n",
-    "    mask2_ = get_binary_mask (mask2_)\n",
-    "    mask3_ = get_binary_mask (mask3_)\n",
-    "    mask4_ = get_binary_mask (mask4_)\n",
-    "    mask5_ = get_binary_mask (mask5_)\n",
-    "    \n",
-    "    ensemble_mask = mask1_ + mask2_ + mask3_ + mask4_ + mask5_\n",
-    "    ensemble_mask[ensemble_mask<=2.0] = 0\n",
-    "    ensemble_mask[ensemble_mask> 2.0] = 1\n",
-    "    \n",
-    "    return ensemble_mask\n",
-    "\n",
-    "def postprocessing_HoleFilling (mask_):\n",
-    "    \n",
-    "    ensemble_mask_post_temp = ndimage.binary_fill_holes(mask_).astype(int)\n",
-    "     \n",
-    "    return ensemble_mask_post_temp\n",
-    "\n",
-    "def get_maximum_index (labeled_array):\n",
-    "    \n",
-    "    ind_nums = []\n",
-    "    for i in range (len(np.unique(labeled_array)) - 1):\n",
-    "        ind_nums.append ([0, i+1])\n",
-    "        \n",
-    "    for i in range (1, len(np.unique(labeled_array))):\n",
-    "        ind_nums[i-1][0] = len(np.where (labeled_array == np.unique(labeled_array)[i])[0])\n",
-    "        \n",
-    "    ind_nums = sorted(ind_nums)\n",
-    "    \n",
-    "    return ind_nums[len(ind_nums)-1][1], ind_nums[len(ind_nums)-2][1]\n",
-    "    \n",
-    "def postprocessing_EliminatingIsolation (ensemble_mask_post_temp):\n",
-    "        \n",
-    "    labeled_array, num_features = label(ensemble_mask_post_temp)\n",
-    "    \n",
-    "    ind_max1, ind_max2 = get_maximum_index (labeled_array)\n",
-    "    \n",
-    "    ensemble_mask_post_temp2 = np.zeros (ensemble_mask_post_temp.shape)\n",
-    "    ensemble_mask_post_temp2[labeled_array == ind_max1] = 1\n",
-    "    ensemble_mask_post_temp2[labeled_array == ind_max2] = 1    \n",
-    "    \n",
-    "    return ensemble_mask_post_temp2.astype(int)\n",
-    "\n",
-    "def get_prediction(model_, img_org_):\n",
-    "    \n",
-    "    img_org_resize = cv2.resize(img_org_,(IMAGE_SIZE[0],IMAGE_SIZE[1]),cv2.INTER_AREA)\n",
-    "    img_org_resize_HE = preprocessing_HE (img_org_resize)    \n",
-    "    img_ready = preprocess_input (img_org_resize_HE)\n",
-    "\n",
-    "    img_ready = np.expand_dims(img_ready, axis=0) \n",
-    "    pr_mask = model_.predict(img_ready)\n",
-    "    pr_mask = np.squeeze(pr_mask)\n",
-    "    pr_mask = np.expand_dims(pr_mask, axis=-1)    \n",
-    "    return pr_mask[:,:,0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ea9ccd8d",
-   "metadata": {},
-   "source": [
-    "# Generation of Masks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e27823f5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for path_ in sorted(glob.glob (path_base_input + '*.*')):\n",
-    "    \n",
-    "    img = cv2.imread(path_)    \n",
-    "    pr_mask1 = get_prediction (model1, img);\n",
-    "    pr_mask2 = get_prediction (model2, img);\n",
-    "    pr_mask3 = get_prediction (model3, img);\n",
-    "    pr_mask4 = get_prediction (model4, img);\n",
-    "    pr_mask5 = get_prediction (model5, img);   \n",
-    "    \n",
-    "    ensemble_mask            = ensemble_results (pr_mask1, pr_mask2, pr_mask3, pr_mask4, pr_mask5)\n",
-    "    ensemble_mask_post_HF    = postprocessing_HoleFilling (ensemble_mask)\n",
-    "    ensemble_mask_post_HF_EI = postprocessing_EliminatingIsolation (ensemble_mask_post_HF)\n",
-    "    \n",
-    "    image_name = path_.split('\\\\')[-1]\n",
-    "    cv2.imwrite(path_base_mask+image_name,ensemble_mask_post_HF_EI*255)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9fd2ba62",
-   "metadata": {},
-   "source": [
-    "# Feature Extraction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "55f861d4",
-   "metadata": {},
-   "source": [
-    "# Defining the path to Images and Image Masks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "ce59d165",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_path = \".\\\\Normal\"\n",
-    "mask_n_path = \".\\\\Normal_Mask\"\n",
-    "p_path = \".\\\\Pneumonia\"\n",
-    "mask_p_path = \".\\\\Pneumonia_Mask\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2ac25014",
-   "metadata": {},
-   "source": [
-    "# Function to load the images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "057bdbd1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def image_files(path,list_name):\n",
-    "    for dir_name, sub_dir_list, file_list in os.walk(path):\n",
-    "        for file_name in file_list:\n",
-    "            if \".jpeg\" in file_name.lower():\n",
-    "                list_name.append(os.path.join(dir_name, file_name))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2849ba99",
-   "metadata": {},
-   "source": [
-    "## Get the Images path stored in a list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "e3491678",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nList = []\n",
-    "image_files(n_path,nList);\n",
-    "\n",
-    "mask_nList = []\n",
-    "image_files(mask_n_path,mask_nList);\n",
-    "\n",
-    "pList = []\n",
-    "image_files(p_path,pList);\n",
-    "\n",
-    "mask_pList = []\n",
-    "image_files(mask_p_path,mask_pList);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c76a5b38",
-   "metadata": {},
-   "source": [
-    "## Create and intantiate Feature Extractor and enable the required features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "4d85b957",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "extractor = radiomics.featureextractor.RadiomicsFeatureExtractor(force2D=True)\n",
-    "extractor.enableImageTypeByName('Original')  # extract features from the original image\n",
-    "extractor.enableFeatureClassByName('firstorder')  # extract first-order features\n",
-    "extractor.enableFeatureClassByName('glcm')  # extract GLCM features\n",
-    "extractor.enableFeatureClassByName('gldm')  # extract GLDM features\n",
-    "extractor.enableFeatureClassByName('glszm')  # extract GLSZM features\n",
-    "extractor.enableFeatureClassByName('ngtdm')  # extract NGTDM features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "8ec8a0fd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "features_name = ['diagnostics_Versions_PyRadiomics',\n",
-    " 'diagnostics_Versions_Numpy',\n",
-    " 'diagnostics_Versions_SimpleITK',\n",
-    " 'diagnostics_Versions_PyWavelet',\n",
-    " 'diagnostics_Versions_Python',\n",
-    " 'diagnostics_Configuration_Settings',\n",
-    " 'diagnostics_Configuration_EnabledImageTypes',\n",
-    " 'diagnostics_Image-original_Hash',\n",
-    " 'diagnostics_Image-original_Dimensionality',\n",
-    " 'diagnostics_Image-original_Spacing',\n",
-    " 'diagnostics_Image-original_Size',\n",
-    " 'diagnostics_Image-original_Mean',\n",
-    " 'diagnostics_Image-original_Minimum',\n",
-    " 'diagnostics_Image-original_Maximum',\n",
-    " 'diagnostics_Mask-original_Hash',\n",
-    " 'diagnostics_Mask-original_Spacing',\n",
-    " 'diagnostics_Mask-original_Size',\n",
-    " 'diagnostics_Mask-original_BoundingBox',\n",
-    " 'diagnostics_Mask-original_VoxelNum',\n",
-    " 'diagnostics_Mask-original_VolumeNum',\n",
-    " 'diagnostics_Mask-original_CenterOfMassIndex',\n",
-    " 'diagnostics_Mask-original_CenterOfMass',\n",
-    " 'original_firstorder_10Percentile',\n",
-    " 'original_firstorder_90Percentile',\n",
-    " 'original_firstorder_Energy',\n",
-    " 'original_firstorder_Entropy',\n",
-    " 'original_firstorder_InterquartileRange',\n",
-    " 'original_firstorder_Kurtosis',\n",
-    " 'original_firstorder_Maximum',\n",
-    " 'original_firstorder_MeanAbsoluteDeviation',\n",
-    " 'original_firstorder_Mean',\n",
-    " 'original_firstorder_Median',\n",
-    " 'original_firstorder_Minimum',\n",
-    " 'original_firstorder_Range',\n",
-    " 'original_firstorder_RobustMeanAbsoluteDeviation',\n",
-    " 'original_firstorder_RootMeanSquared',\n",
-    " 'original_firstorder_Skewness',\n",
-    " 'original_firstorder_TotalEnergy',\n",
-    " 'original_firstorder_Uniformity',\n",
-    " 'original_firstorder_Variance',\n",
-    " 'original_glcm_Autocorrelation',\n",
-    " 'original_glcm_ClusterProminence',\n",
-    " 'original_glcm_ClusterShade',\n",
-    " 'original_glcm_ClusterTendency',\n",
-    " 'original_glcm_Contrast',\n",
-    " 'original_glcm_Correlation',\n",
-    " 'original_glcm_DifferenceAverage',\n",
-    " 'original_glcm_DifferenceEntropy',\n",
-    " 'original_glcm_DifferenceVariance',\n",
-    " 'original_glcm_Id',\n",
-    " 'original_glcm_Idm',\n",
-    " 'original_glcm_Idmn',\n",
-    " 'original_glcm_Idn',\n",
-    " 'original_glcm_Imc1',\n",
-    " 'original_glcm_Imc2',\n",
-    " 'original_glcm_InverseVariance',\n",
-    " 'original_glcm_JointAverage',\n",
-    " 'original_glcm_JointEnergy',\n",
-    " 'original_glcm_JointEntropy',\n",
-    " 'original_glcm_MCC',\n",
-    " 'original_glcm_MaximumProbability',\n",
-    " 'original_glcm_SumAverage',\n",
-    " 'original_glcm_SumEntropy',\n",
-    " 'original_glcm_SumSquares',\n",
-    " 'original_gldm_DependenceEntropy',\n",
-    " 'original_gldm_DependenceNonUniformity',\n",
-    " 'original_gldm_DependenceNonUniformityNormalized',\n",
-    " 'original_gldm_DependenceVariance',\n",
-    " 'original_gldm_GrayLevelNonUniformity',\n",
-    " 'original_gldm_GrayLevelVariance',\n",
-    " 'original_gldm_HighGrayLevelEmphasis',\n",
-    " 'original_gldm_LargeDependenceEmphasis',\n",
-    " 'original_gldm_LargeDependenceHighGrayLevelEmphasis',\n",
-    " 'original_gldm_LargeDependenceLowGrayLevelEmphasis',\n",
-    " 'original_gldm_LowGrayLevelEmphasis',\n",
-    " 'original_gldm_SmallDependenceEmphasis',\n",
-    " 'original_gldm_SmallDependenceHighGrayLevelEmphasis',\n",
-    " 'original_gldm_SmallDependenceLowGrayLevelEmphasis',\n",
-    " 'original_glrlm_GrayLevelNonUniformity',\n",
-    " 'original_glrlm_GrayLevelNonUniformityNormalized',\n",
-    " 'original_glrlm_GrayLevelVariance',\n",
-    " 'original_glrlm_HighGrayLevelRunEmphasis',\n",
-    " 'original_glrlm_LongRunEmphasis',\n",
-    " 'original_glrlm_LongRunHighGrayLevelEmphasis',\n",
-    " 'original_glrlm_LongRunLowGrayLevelEmphasis',\n",
-    " 'original_glrlm_LowGrayLevelRunEmphasis',\n",
-    " 'original_glrlm_RunEntropy',\n",
-    " 'original_glrlm_RunLengthNonUniformity',\n",
-    " 'original_glrlm_RunLengthNonUniformityNormalized',\n",
-    " 'original_glrlm_RunPercentage',\n",
-    " 'original_glrlm_RunVariance',\n",
-    " 'original_glrlm_ShortRunEmphasis',\n",
-    " 'original_glrlm_ShortRunHighGrayLevelEmphasis',\n",
-    " 'original_glrlm_ShortRunLowGrayLevelEmphasis',\n",
-    " 'original_glszm_GrayLevelNonUniformity',\n",
-    " 'original_glszm_GrayLevelNonUniformityNormalized',\n",
-    " 'original_glszm_GrayLevelVariance',\n",
-    " 'original_glszm_HighGrayLevelZoneEmphasis',\n",
-    " 'original_glszm_LargeAreaEmphasis',\n",
-    " 'original_glszm_LargeAreaHighGrayLevelEmphasis',\n",
-    " 'original_glszm_LargeAreaLowGrayLevelEmphasis',\n",
-    " 'original_glszm_LowGrayLevelZoneEmphasis',\n",
-    " 'original_glszm_SizeZoneNonUniformity',\n",
-    " 'original_glszm_SizeZoneNonUniformityNormalized',\n",
-    " 'original_glszm_SmallAreaEmphasis',\n",
-    " 'original_glszm_SmallAreaHighGrayLevelEmphasis',\n",
-    " 'original_glszm_SmallAreaLowGrayLevelEmphasis',\n",
-    " 'original_glszm_ZoneEntropy',\n",
-    " 'original_glszm_ZonePercentage',\n",
-    " 'original_glszm_ZoneVariance',\n",
-    " 'original_ngtdm_Busyness',\n",
-    " 'original_ngtdm_Coarseness',\n",
-    " 'original_ngtdm_Complexity',\n",
-    " 'original_ngtdm_Contrast',\n",
-    " 'original_ngtdm_Strength']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "606a4177",
-   "metadata": {},
-   "source": [
-    "# Function to Generate features and dataframe"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "434d3ad3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def extract_features_get_DataFrame(imageList,maskList,LabelName):\n",
-    "    features = {}\n",
-    "    data = []\n",
-    "    df = pd.DataFrame(columns=features_name)\n",
-    "    \n",
-    "    for i in range(len(imageList)):\n",
-    "        imagepath = imageList[i]\n",
-    "        maskpath = maskList[i]\n",
-    "\n",
-    "        # Load image and mask using SimpleITK\n",
-    "        image = sitk.ReadImage(imagepath, sitk.sitkInt8)\n",
-    "        mask = sitk.ReadImage(maskpath, sitk.sitkInt8)\n",
-    "        features[i] = extractor.execute(image, mask)\n",
-    "\n",
-    "        values = []\n",
-    "        for key, value in six.iteritems(features[i]):\n",
-    "            values.append(value)\n",
-    "        data.extend([values])\n",
-    "        df.loc[i] = data[i]\n",
-    "    df['Label'] = LabelName\n",
-    "    DataFrame = df.iloc[:, 22:]\n",
-    "    return DataFrame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0364ebd4",
-   "metadata": {},
-   "source": [
-    "### For Normal Images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "a2093d49",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Shape features are only available 3D input (for 2D input, use shape2D). Found 2D input\n",
-      "GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n",
-      "Shape features are only available 3D input (for 2D input, use shape2D). Found 2D input\n",
-      "GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n",
-      "Shape features are only available 3D input (for 2D input, use shape2D). Found 2D input\n",
-      "GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n",
-      "Shape features are only available 3D input (for 2D input, use shape2D). Found 2D input\n",
-      "GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n",
-      "Shape features are only available 3D input (for 2D input, use shape2D). Found 2D input\n",
-      "GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_Normal = extract_features_get_DataFrame(nList,mask_nList,'0')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "8bf50719",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>original_firstorder_10Percentile</th>\n",
-       "      <th>original_firstorder_90Percentile</th>\n",
-       "      <th>original_firstorder_Energy</th>\n",
-       "      <th>original_firstorder_Entropy</th>\n",
-       "      <th>original_firstorder_InterquartileRange</th>\n",
-       "      <th>original_firstorder_Kurtosis</th>\n",
-       "      <th>original_firstorder_Maximum</th>\n",
-       "      <th>original_firstorder_MeanAbsoluteDeviation</th>\n",
-       "      <th>original_firstorder_Mean</th>\n",
-       "      <th>original_firstorder_Median</th>\n",
-       "      <th>...</th>\n",
-       "      <th>original_glszm_SmallAreaLowGrayLevelEmphasis</th>\n",
-       "      <th>original_glszm_ZoneEntropy</th>\n",
-       "      <th>original_glszm_ZonePercentage</th>\n",
-       "      <th>original_glszm_ZoneVariance</th>\n",
-       "      <th>original_ngtdm_Busyness</th>\n",
-       "      <th>original_ngtdm_Coarseness</th>\n",
-       "      <th>original_ngtdm_Complexity</th>\n",
-       "      <th>original_ngtdm_Contrast</th>\n",
-       "      <th>original_ngtdm_Strength</th>\n",
-       "      <th>Label</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>-112.0</td>\n",
-       "      <td>-34.0</td>\n",
-       "      <td>5550652.0</td>\n",
-       "      <td>2.3284534869438067</td>\n",
-       "      <td>44.5</td>\n",
-       "      <td>8.400887583892088</td>\n",
-       "      <td>127.0</td>\n",
-       "      <td>34.82461752409011</td>\n",
-       "      <td>-66.45515394912985</td>\n",
-       "      <td>-77.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.11973022960113251</td>\n",
-       "      <td>2.7722970475535966</td>\n",
-       "      <td>0.9183400267737617</td>\n",
-       "      <td>0.09267609584441858</td>\n",
-       "      <td>0.6286417322834645</td>\n",
-       "      <td>0.05847815875998121</td>\n",
-       "      <td>4.4602144186156165</td>\n",
-       "      <td>0.017935731294027306</td>\n",
-       "      <td>4.475337246421584</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-111.5</td>\n",
-       "      <td>-27.0</td>\n",
-       "      <td>4925398.0</td>\n",
-       "      <td>2.4460343300299066</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>8.624428344572735</td>\n",
-       "      <td>127.0</td>\n",
-       "      <td>32.96998181864095</td>\n",
-       "      <td>-61.69095477386934</td>\n",
-       "      <td>-67.5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.10569254922967047</td>\n",
-       "      <td>2.9141566594543917</td>\n",
-       "      <td>0.9108040201005025</td>\n",
-       "      <td>0.11040951248513674</td>\n",
-       "      <td>0.5899321266968326</td>\n",
-       "      <td>0.04625352276358988</td>\n",
-       "      <td>9.80436059519249</td>\n",
-       "      <td>0.022246815252840835</td>\n",
-       "      <td>2.4286386615858753</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-112.0</td>\n",
-       "      <td>-39.0</td>\n",
-       "      <td>6671289.0</td>\n",
-       "      <td>2.3267621954806588</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>9.616482566993795</td>\n",
-       "      <td>127.0</td>\n",
-       "      <td>32.461620520299924</td>\n",
-       "      <td>-68.02552719200888</td>\n",
-       "      <td>-78.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.11589114921160931</td>\n",
-       "      <td>2.819440707913354</td>\n",
-       "      <td>0.9078801331853497</td>\n",
-       "      <td>0.10339638093985569</td>\n",
-       "      <td>1.4055338757887745</td>\n",
-       "      <td>0.026612319643199975</td>\n",
-       "      <td>10.437463435255335</td>\n",
-       "      <td>0.043792505681648095</td>\n",
-       "      <td>1.4309324118676843</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-118.0</td>\n",
-       "      <td>106.0</td>\n",
-       "      <td>5864917.0</td>\n",
-       "      <td>2.5719474080056024</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>4.704081014241062</td>\n",
-       "      <td>127.0</td>\n",
-       "      <td>48.106868838649454</td>\n",
-       "      <td>-56.4366391184573</td>\n",
-       "      <td>-72.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.1314703103204144</td>\n",
-       "      <td>3.082502248430138</td>\n",
-       "      <td>0.9022038567493113</td>\n",
-       "      <td>0.11496765922731775</td>\n",
-       "      <td>0.48974369472649854</td>\n",
-       "      <td>0.06069472892195795</td>\n",
-       "      <td>9.424465415358583</td>\n",
-       "      <td>0.04455113541805542</td>\n",
-       "      <td>3.1087223587223587</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-118.0</td>\n",
-       "      <td>-14.799999999999955</td>\n",
-       "      <td>5662306.0</td>\n",
-       "      <td>2.571356314779426</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>6.004405529018538</td>\n",
-       "      <td>127.0</td>\n",
-       "      <td>41.91748715028355</td>\n",
-       "      <td>-61.28281461434371</td>\n",
-       "      <td>-75.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.12574045993337593</td>\n",
-       "      <td>2.97907116578555</td>\n",
-       "      <td>0.925575101488498</td>\n",
-       "      <td>0.08563959850894291</td>\n",
-       "      <td>0.5294346840464722</td>\n",
-       "      <td>0.049441359470127774</td>\n",
-       "      <td>11.406539458175532</td>\n",
-       "      <td>0.02805927961079095</td>\n",
-       "      <td>3.3538475417230496</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 94 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  original_firstorder_10Percentile original_firstorder_90Percentile  \\\n",
-       "0                           -112.0                            -34.0   \n",
-       "1                           -111.5                            -27.0   \n",
-       "2                           -112.0                            -39.0   \n",
-       "3                           -118.0                            106.0   \n",
-       "4                           -118.0              -14.799999999999955   \n",
-       "\n",
-       "  original_firstorder_Energy original_firstorder_Entropy  \\\n",
-       "0                  5550652.0          2.3284534869438067   \n",
-       "1                  4925398.0          2.4460343300299066   \n",
-       "2                  6671289.0          2.3267621954806588   \n",
-       "3                  5864917.0          2.5719474080056024   \n",
-       "4                  5662306.0           2.571356314779426   \n",
-       "\n",
-       "  original_firstorder_InterquartileRange original_firstorder_Kurtosis  \\\n",
-       "0                                   44.5            8.400887583892088   \n",
-       "1                                   50.0            8.624428344572735   \n",
-       "2                                   41.0            9.616482566993795   \n",
-       "3                                   56.0            4.704081014241062   \n",
-       "4                                   52.0            6.004405529018538   \n",
-       "\n",
-       "  original_firstorder_Maximum original_firstorder_MeanAbsoluteDeviation  \\\n",
-       "0                       127.0                         34.82461752409011   \n",
-       "1                       127.0                         32.96998181864095   \n",
-       "2                       127.0                        32.461620520299924   \n",
-       "3                       127.0                        48.106868838649454   \n",
-       "4                       127.0                         41.91748715028355   \n",
-       "\n",
-       "  original_firstorder_Mean original_firstorder_Median  ...  \\\n",
-       "0       -66.45515394912985                      -77.0  ...   \n",
-       "1       -61.69095477386934                      -67.5  ...   \n",
-       "2       -68.02552719200888                      -78.0  ...   \n",
-       "3        -56.4366391184573                      -72.0  ...   \n",
-       "4       -61.28281461434371                      -75.0  ...   \n",
-       "\n",
-       "  original_glszm_SmallAreaLowGrayLevelEmphasis original_glszm_ZoneEntropy  \\\n",
-       "0                          0.11973022960113251         2.7722970475535966   \n",
-       "1                          0.10569254922967047         2.9141566594543917   \n",
-       "2                          0.11589114921160931          2.819440707913354   \n",
-       "3                           0.1314703103204144          3.082502248430138   \n",
-       "4                          0.12574045993337593           2.97907116578555   \n",
-       "\n",
-       "  original_glszm_ZonePercentage original_glszm_ZoneVariance  \\\n",
-       "0            0.9183400267737617         0.09267609584441858   \n",
-       "1            0.9108040201005025         0.11040951248513674   \n",
-       "2            0.9078801331853497         0.10339638093985569   \n",
-       "3            0.9022038567493113         0.11496765922731775   \n",
-       "4             0.925575101488498         0.08563959850894291   \n",
-       "\n",
-       "  original_ngtdm_Busyness original_ngtdm_Coarseness original_ngtdm_Complexity  \\\n",
-       "0      0.6286417322834645       0.05847815875998121        4.4602144186156165   \n",
-       "1      0.5899321266968326       0.04625352276358988          9.80436059519249   \n",
-       "2      1.4055338757887745      0.026612319643199975        10.437463435255335   \n",
-       "3     0.48974369472649854       0.06069472892195795         9.424465415358583   \n",
-       "4      0.5294346840464722      0.049441359470127774        11.406539458175532   \n",
-       "\n",
-       "  original_ngtdm_Contrast original_ngtdm_Strength Label  \n",
-       "0    0.017935731294027306       4.475337246421584     0  \n",
-       "1    0.022246815252840835      2.4286386615858753     0  \n",
-       "2    0.043792505681648095      1.4309324118676843     0  \n",
-       "3     0.04455113541805542      3.1087223587223587     0  \n",
-       "4     0.02805927961079095      3.3538475417230496     0  \n",
-       "\n",
-       "[5 rows x 94 columns]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_Normal.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "17117b6f",
-   "metadata": {},
-   "source": [
-    "### For Pneumonia Images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "ce6bc9e1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Shape features are only available 3D input (for 2D input, use shape2D). Found 2D input\n",
-      "GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n",
-      "Shape features are only available 3D input (for 2D input, use shape2D). Found 2D input\n",
-      "GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n",
-      "Shape features are only available 3D input (for 2D input, use shape2D). Found 2D input\n",
-      "GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n",
-      "Shape features are only available 3D input (for 2D input, use shape2D). Found 2D input\n",
-      "GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n",
-      "Shape features are only available 3D input (for 2D input, use shape2D). Found 2D input\n",
-      "GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n",
-      "Shape features are only available 3D input (for 2D input, use shape2D). Found 2D input\n",
-      "GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n",
-      "Shape features are only available 3D input (for 2D input, use shape2D). Found 2D input\n",
-      "GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n",
-      "Shape features are only available 3D input (for 2D input, use shape2D). Found 2D input\n",
-      "GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n",
-      "Shape features are only available 3D input (for 2D input, use shape2D). Found 2D input\n",
-      "GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n",
-      "Shape features are only available 3D input (for 2D input, use shape2D). Found 2D input\n",
-      "GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_Pneumonia = extract_features_get_DataFrame(pList,mask_pList,'1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "aa249857",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>original_firstorder_10Percentile</th>\n",
-       "      <th>original_firstorder_90Percentile</th>\n",
-       "      <th>original_firstorder_Energy</th>\n",
-       "      <th>original_firstorder_Entropy</th>\n",
-       "      <th>original_firstorder_InterquartileRange</th>\n",
-       "      <th>original_firstorder_Kurtosis</th>\n",
-       "      <th>original_firstorder_Maximum</th>\n",
-       "      <th>original_firstorder_MeanAbsoluteDeviation</th>\n",
-       "      <th>original_firstorder_Mean</th>\n",
-       "      <th>original_firstorder_Median</th>\n",
-       "      <th>...</th>\n",
-       "      <th>original_glszm_SmallAreaLowGrayLevelEmphasis</th>\n",
-       "      <th>original_glszm_ZoneEntropy</th>\n",
-       "      <th>original_glszm_ZonePercentage</th>\n",
-       "      <th>original_glszm_ZoneVariance</th>\n",
-       "      <th>original_ngtdm_Busyness</th>\n",
-       "      <th>original_ngtdm_Coarseness</th>\n",
-       "      <th>original_ngtdm_Complexity</th>\n",
-       "      <th>original_ngtdm_Contrast</th>\n",
-       "      <th>original_ngtdm_Strength</th>\n",
-       "      <th>Label</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>-120.6</td>\n",
-       "      <td>108.60000000000002</td>\n",
-       "      <td>6097512.0</td>\n",
-       "      <td>2.057964519759019</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>5.119681336620311</td>\n",
-       "      <td>127.0</td>\n",
-       "      <td>51.04687788279773</td>\n",
-       "      <td>-69.9895652173913</td>\n",
-       "      <td>-96.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.15919477040215677</td>\n",
-       "      <td>2.5675195873233774</td>\n",
-       "      <td>0.9043478260869565</td>\n",
-       "      <td>0.11765902366863906</td>\n",
-       "      <td>0.5239669421487604</td>\n",
-       "      <td>0.06478143307796305</td>\n",
-       "      <td>6.937011985872065</td>\n",
-       "      <td>0.05134519235872208</td>\n",
-       "      <td>6.09080398348312</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-116.0</td>\n",
-       "      <td>-70.0</td>\n",
-       "      <td>6461289.0</td>\n",
-       "      <td>1.667250935600759</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>28.564360319356922</td>\n",
-       "      <td>127.0</td>\n",
-       "      <td>17.958286205492467</td>\n",
-       "      <td>-88.63814866760168</td>\n",
-       "      <td>-94.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.1405653396247152</td>\n",
-       "      <td>2.2156836812477056</td>\n",
-       "      <td>0.8948106591865358</td>\n",
-       "      <td>0.12567928774284845</td>\n",
-       "      <td>0.7631559497413155</td>\n",
-       "      <td>0.06905234613335916</td>\n",
-       "      <td>2.77958442124216</td>\n",
-       "      <td>0.011807342060622953</td>\n",
-       "      <td>5.121136155315789</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-120.0</td>\n",
-       "      <td>113.0</td>\n",
-       "      <td>7163624.0</td>\n",
-       "      <td>1.7458941724671342</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>5.030471037246626</td>\n",
-       "      <td>127.0</td>\n",
-       "      <td>54.50320958312556</td>\n",
-       "      <td>-74.14657980456026</td>\n",
-       "      <td>-104.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.18805170757506612</td>\n",
-       "      <td>2.339753028342179</td>\n",
-       "      <td>0.8892508143322475</td>\n",
-       "      <td>0.1456614203866951</td>\n",
-       "      <td>1.1731795145372097</td>\n",
-       "      <td>0.03490024441539249</td>\n",
-       "      <td>8.892820945484297</td>\n",
-       "      <td>0.1006570532853642</td>\n",
-       "      <td>3.828229346144656</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-126.0</td>\n",
-       "      <td>124.0</td>\n",
-       "      <td>5946172.0</td>\n",
-       "      <td>1.917296573002114</td>\n",
-       "      <td>238.0</td>\n",
-       "      <td>1.2631965842701234</td>\n",
-       "      <td>127.0</td>\n",
-       "      <td>110.64774472687941</td>\n",
-       "      <td>-27.43720930232558</td>\n",
-       "      <td>-108.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.22895996835772423</td>\n",
-       "      <td>2.3491117375455075</td>\n",
-       "      <td>0.9232558139534883</td>\n",
-       "      <td>0.08125170516912104</td>\n",
-       "      <td>0.6489583333333333</td>\n",
-       "      <td>0.04060050986686809</td>\n",
-       "      <td>12.309788327731372</td>\n",
-       "      <td>0.4868604189987465</td>\n",
-       "      <td>3.329868949322021</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-121.0</td>\n",
-       "      <td>122.0</td>\n",
-       "      <td>6204270.0</td>\n",
-       "      <td>2.506298357482142</td>\n",
-       "      <td>218.0</td>\n",
-       "      <td>1.1766896090537295</td>\n",
-       "      <td>127.0</td>\n",
-       "      <td>104.01235814782083</td>\n",
-       "      <td>16.262172284644194</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.12010991140531248</td>\n",
-       "      <td>2.9129073477941847</td>\n",
-       "      <td>0.9288389513108615</td>\n",
-       "      <td>0.07477562434963579</td>\n",
-       "      <td>0.19821798383325542</td>\n",
-       "      <td>0.0449930488267262</td>\n",
-       "      <td>18.92690939073711</td>\n",
-       "      <td>0.0874645985221415</td>\n",
-       "      <td>4.8346297896859705</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 94 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  original_firstorder_10Percentile original_firstorder_90Percentile  \\\n",
-       "0                           -120.6               108.60000000000002   \n",
-       "1                           -116.0                            -70.0   \n",
-       "2                           -120.0                            113.0   \n",
-       "3                           -126.0                            124.0   \n",
-       "4                           -121.0                            122.0   \n",
-       "\n",
-       "  original_firstorder_Energy original_firstorder_Entropy  \\\n",
-       "0                  6097512.0           2.057964519759019   \n",
-       "1                  6461289.0           1.667250935600759   \n",
-       "2                  7163624.0          1.7458941724671342   \n",
-       "3                  5946172.0           1.917296573002114   \n",
-       "4                  6204270.0           2.506298357482142   \n",
-       "\n",
-       "  original_firstorder_InterquartileRange original_firstorder_Kurtosis  \\\n",
-       "0                                   32.0            5.119681336620311   \n",
-       "1                                   24.0           28.564360319356922   \n",
-       "2                                   21.0            5.030471037246626   \n",
-       "3                                  238.0           1.2631965842701234   \n",
-       "4                                  218.0           1.1766896090537295   \n",
-       "\n",
-       "  original_firstorder_Maximum original_firstorder_MeanAbsoluteDeviation  \\\n",
-       "0                       127.0                         51.04687788279773   \n",
-       "1                       127.0                        17.958286205492467   \n",
-       "2                       127.0                         54.50320958312556   \n",
-       "3                       127.0                        110.64774472687941   \n",
-       "4                       127.0                        104.01235814782083   \n",
-       "\n",
-       "  original_firstorder_Mean original_firstorder_Median  ...  \\\n",
-       "0        -69.9895652173913                      -96.0  ...   \n",
-       "1       -88.63814866760168                      -94.0  ...   \n",
-       "2       -74.14657980456026                     -104.0  ...   \n",
-       "3       -27.43720930232558                     -108.0  ...   \n",
-       "4       16.262172284644194                       90.0  ...   \n",
-       "\n",
-       "  original_glszm_SmallAreaLowGrayLevelEmphasis original_glszm_ZoneEntropy  \\\n",
-       "0                          0.15919477040215677         2.5675195873233774   \n",
-       "1                           0.1405653396247152         2.2156836812477056   \n",
-       "2                          0.18805170757506612          2.339753028342179   \n",
-       "3                          0.22895996835772423         2.3491117375455075   \n",
-       "4                          0.12010991140531248         2.9129073477941847   \n",
-       "\n",
-       "  original_glszm_ZonePercentage original_glszm_ZoneVariance  \\\n",
-       "0            0.9043478260869565         0.11765902366863906   \n",
-       "1            0.8948106591865358         0.12567928774284845   \n",
-       "2            0.8892508143322475          0.1456614203866951   \n",
-       "3            0.9232558139534883         0.08125170516912104   \n",
-       "4            0.9288389513108615         0.07477562434963579   \n",
-       "\n",
-       "  original_ngtdm_Busyness original_ngtdm_Coarseness original_ngtdm_Complexity  \\\n",
-       "0      0.5239669421487604       0.06478143307796305         6.937011985872065   \n",
-       "1      0.7631559497413155       0.06905234613335916          2.77958442124216   \n",
-       "2      1.1731795145372097       0.03490024441539249         8.892820945484297   \n",
-       "3      0.6489583333333333       0.04060050986686809        12.309788327731372   \n",
-       "4     0.19821798383325542        0.0449930488267262         18.92690939073711   \n",
-       "\n",
-       "  original_ngtdm_Contrast original_ngtdm_Strength Label  \n",
-       "0     0.05134519235872208        6.09080398348312     1  \n",
-       "1    0.011807342060622953       5.121136155315789     1  \n",
-       "2      0.1006570532853642       3.828229346144656     1  \n",
-       "3      0.4868604189987465       3.329868949322021     1  \n",
-       "4      0.0874645985221415      4.8346297896859705     1  \n",
-       "\n",
-       "[5 rows x 94 columns]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_Pneumonia.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "745c6e3e",
-   "metadata": {},
-   "source": [
-    "## Function to Convert generated dataframes to CSV files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "d739bc9e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_CSVfile(Normal_DataFrame,Pneumonia_DataFrame,path_to_CSV):\n",
-    "    final_df = pd.concat([Normal_DataFrame, Pneumonia_DataFrame], axis=0)\n",
-    "    final_df.reset_index(drop=True, inplace=True)\n",
-    "    return final_df.to_csv(path_to_CSV)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "6665dc08",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "get_CSVfile(df_Normal,df_Pneumonia,\"final_dataset.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "0297a795",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5856, 94)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.read_csv('final_dataset.csv')\n",
-    "df.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "76ebd511",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((4684, 93), (1172, 93))"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(\n",
-    "    df.drop(labels=['Label'], axis=1),\n",
-    "    df['Label'],\n",
-    "    test_size=0.2,\n",
-    "    random_state=23)\n",
-    "\n",
-    "X_train.shape, X_test.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "a6da1916",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.preprocessing import StandardScaler\n",
-    "def scale_data(dataset):\n",
-    "    scaler = StandardScaler()\n",
-    "    scaled_data = scaler.fit_transform(dataset)\n",
-    "    return scaled_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "0aba39a2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scaled_train_set = scale_data(X_train)\n",
-    "\n",
-    "scaled_test_set = scale_data(X_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "bc7227f4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Perceptron()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Perceptron</label><div class=\"sk-toggleable__content\"><pre>Perceptron()</pre></div></div></div></div></div>"
-      ],
-      "text/plain": [
-       "Perceptron()"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "PClr = Perceptron(tol=1e-3, random_state=0)\n",
-    "PClr.fit(scaled_train_set,y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "afb578b5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, max_features=&#x27;log2&#x27;,\n",
-       "                       min_samples_split=4, n_estimators=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, max_features=&#x27;log2&#x27;,\n",
-       "                       min_samples_split=4, n_estimators=200)</pre></div></div></div></div></div>"
-      ],
-      "text/plain": [
-       "RandomForestClassifier(criterion='entropy', max_features='log2',\n",
-       "                       min_samples_split=4, n_estimators=200)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "RF = RandomForestClassifier(criterion = 'entropy',max_depth = None, max_features = 'log2', max_leaf_nodes = None,\n",
-    "                               min_samples_leaf = 1,min_samples_split = 4, min_weight_fraction_leaf = 0.0, n_estimators = 200)\n",
-    "RF.fit(X_train,y_train) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "eac03123",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pickle\n",
-    "# create an iterator object with write permission - model.pkl\n",
-    "with open('model_pkl', 'wb') as files:\n",
-    "    pickle.dump(RF, files)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "b959ce43",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.8967576791808873"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = pickle.load(open('model_pkl', 'rb'))\n",
-    "model.score(X_test,y_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "af8bab7d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# model.predict('C:\\Users\\Dell\\OneDrive\\Desktop\\Files\\Demo\\Normal\\IM-0001-0001.jpeg')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "20db76e6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 1, 1, ..., 0, 1, 0], dtype=int64)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "RF.predict(X_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "bd5bbd4b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.8967576791808873"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "RF.score(X_test,y_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "30617b5e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from sklearn.preprocessing import MinMaxScaler"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "e769fda3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def pneumonia(image):\n",
-    "    img_ = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
-    "    resized_img = cv2.resize(img_,(256,256))\n",
-    "    original_image = 'original.jpeg'\n",
-    "    cv2.imwrite(original_image,resized_img)\n",
-    "    img = cv2.imread(original_image)\n",
-    "    pr_mask1 = get_prediction (model1, img);\n",
-    "    pr_mask2 = get_prediction (model2, img);\n",
-    "    pr_mask3 = get_prediction (model3, img);\n",
-    "    pr_mask4 = get_prediction (model4, img);\n",
-    "    pr_mask5 = get_prediction (model5, img);   \n",
-    "    \n",
-    "    ensemble_mask            = ensemble_results (pr_mask1, pr_mask2, pr_mask3, pr_mask4, pr_mask5)\n",
-    "    ensemble_mask_post_HF    = postprocessing_HoleFilling (ensemble_mask)\n",
-    "    ensemble_mask_post_HF_EI = postprocessing_EliminatingIsolation (ensemble_mask_post_HF)\n",
-    "  \n",
-    "    mask = 'mask.jpeg'\n",
-    "    cv2.imwrite(mask,ensemble_mask_post_HF_EI*255)\n",
-    "  \n",
-    "    features = {}\n",
-    "    df = pd.DataFrame(columns=features_name)\n",
-    "    \n",
-    "    image_ = sitk.ReadImage(original_image, sitk.sitkInt8)\n",
-    "    mask = sitk.ReadImage(mask, sitk.sitkInt8)\n",
-    "    features = extractor.execute(image_, mask)\n",
-    "    \n",
-    "    df = df.append(features, ignore_index=True)\n",
-    "    cols = df.columns[22:]\n",
-    "\n",
-    "    # Create new dataframe with selected columns\n",
-    "    DataFrame = df[cols]\n",
-    "\n",
-    "    prediction = model.predict(DataFrame)\n",
-    "\n",
-    "    if prediction == 0: # Determine the predicted class\n",
-    "        Label = \"Normal\"\n",
-    "\n",
-    "    elif prediction == 1:\n",
-    "        Label = \"Pneumonia\"\n",
-    "\n",
-    "    return Label"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "1e41b863",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running on local URL:  http://127.0.0.1:7867\n",
-      "\n",
-      "Could not create share link. Please check your internet connection or our status page: https://status.gradio.app\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><iframe src=\"http://127.0.0.1:7867/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 280ms/step\n",
-      "1/1 [==============================] - 0s 300ms/step\n",
-      "1/1 [==============================] - 0s 284ms/step\n",
-      "1/1 [==============================] - 1s 899ms/step\n",
-      "1/1 [==============================] - 1s 906ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Shape features are only available 3D input (for 2D input, use shape2D). Found 2D input\n",
-      "GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 273ms/step\n",
-      "1/1 [==============================] - 0s 278ms/step\n",
-      "1/1 [==============================] - 0s 277ms/step\n",
-      "1/1 [==============================] - 1s 904ms/step\n",
-      "1/1 [==============================] - 1s 920ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Shape features are only available 3D input (for 2D input, use shape2D). Found 2D input\n",
-      "GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 269ms/step\n",
-      "1/1 [==============================] - 0s 279ms/step\n",
-      "1/1 [==============================] - 0s 285ms/step\n",
-      "1/1 [==============================] - 1s 889ms/step\n",
-      "1/1 [==============================] - 1s 873ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Shape features are only available 3D input (for 2D input, use shape2D). Found 2D input\n",
-      "GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 272ms/step\n",
-      "1/1 [==============================] - 0s 286ms/step\n",
-      "1/1 [==============================] - 0s 289ms/step\n",
-      "1/1 [==============================] - 1s 878ms/step\n",
-      "1/1 [==============================] - 1s 857ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Shape features are only available 3D input (for 2D input, use shape2D). Found 2D input\n",
-      "GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n"
-     ]
-    }
-   ],
-   "source": [
-    "import gradio as gr\n",
-    "iface =gr.Interface(fn = pneumonia,\n",
-    "                    inputs = \"image\",\n",
-    "                    outputs = [gr.outputs.Textbox(label=\"Prediction\")],\n",
-    "                    title = \"Pnuemonia Detection from Chest X ray images\",\n",
-    "                    description = \"Upload a Chest X ray image\")\n",
-    "iface.launch(share = True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "92a2b027",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}