Data Preparation for Training
In this section, we will convert the given images into NumPy arrays of their pixels after resizing them because training a Deep Neural Network on large-size images is highly inefficient in terms of computational cost and time.
For this purpose, we will use the OpenCV library and Numpy library of python to serve the purpose. Also, after all the images are converted into the desired format we will split them into training and validation data so, that we can evaluate the performance of our model.
Python3
IMG_SIZE = 256SPLIT = 0.2EPOCHS = 10BATCH_SIZE = 64 |
Some of the hyperparameters we can tweak from here for the whole notebook.
Python3
X = [] Y = [] for i, cat in enumerate(classes): images = glob(f'{path}/{cat}/*.jpeg') for image in images: img = cv2.imread(image) X.append(cv2.resize(img, (IMG_SIZE, IMG_SIZE))) Y.append(i) X = np.asarray(X) one_hot_encoded_Y = pd.get_dummies(Y).values |
One hot encoding will help us to train a model which can predict soft probabilities of an image being from each class with the highest probability for the class to which it really belongs.
Python3
X_train, X_val, Y_train, Y_val = train_test_split( X, one_hot_encoded_Y, test_size = SPLIT, random_state = 2022) print(X_train.shape, X_val.shape) |
Output:
(12000, 256, 256, 3) (3000, 256, 256, 3)
In this step, we will achieve the shuffling of the data automatically because the train_test_split function split the data randomly in the given ratio.
Lung Cancer Detection Using Transfer Learning
Computer Vision is one of the applications of deep neural networks that enables us to automate tasks that earlier required years of expertise and one such use in predicting the presence of cancerous cells.
In this article, we will learn how to build a classifier using the Transfer Learning technique which can classify normal lung tissues from cancerous. This project has been developed using collab and the dataset has been taken from Kaggle whose link has been provided as well.