Computer Vision News - May 2021

15 Class Imbalance in Classification Tasks Only 3 of the 15 classes (classification of diseases) available in the full dataset have been selected for this experiment. These are Fibrosis, Effusion and Atelectasis. Let’s first have a look at the original class distribution across them: from keras.callbacks import TensorBoard from keras.preprocessing import image from keras.layers import Dropout, Flatten, Activation, Dense from keras.constraints import maxnorm from keras.optimizers import SGD, RMSprop from keras.layers import Convolution2D, ZeroPadding2D from keras.layers.convolutional import MaxPooling2D from keras.utils import np_utils from keras import backend as K from keras.models import Sequential from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report from tqdm import tqdm from sklearn.utils import class_weight from sklearn.model_selection import KFold, StratifiedKFold from keras.wrappers.scikit_learn import KerasClassifier from sklearn.model_selection import cross_val_score from single_label_model import create_model from utils import * from imblearn.under_sampling import RandomUnderSampler, TomekLinks from imblearn.over_sampling import RandomOverSampler, SMOTE from sklearn.preprocessing import LabelEncoder from collections import Counter # Read csv file and display format train = pd.read_csv('./archive/sample_labels.csv') print (train.head()) options = ['Atelectasis','Effusion','Fibrosis'] new_train = train[train['Finding Labels'].isin(options)] new_train = new_train.reset_index(drop=True) print (new_train.shape[ 0 ]) # Load training images train_image = [] for i in tqdm(range(new_train.shape[ 0 ])): # train.shape[0] name = new_train['Image Index'][i] img = image.load_img('./archive/sample/images/'+name, target_size=( 150 , 150 , 3 )) img = image.img_to_array(img) img = img.astype('float32') img = img/ 255.0 train_image.append(img) X = np.array(train_image)