What is the objective of the project of lesson 2 of Creating dataset?

• Build a product that helps doctors quickly identify cases of pneumonia in children

• Build a product that helps people quickly identify cases of pneumonia in young people

• Sales forecasting using Machine Learning

• Classification of Cancer patient

Which important factors does the classification model of the optional project classify?

• Can help flag serious cases of pneumonia

• Can predict serious cases of corona virus

• Quickly identify healthy cases of pneumonia

• Can identify negative cases of corona virus

From which the dataset is taken?

• Github

• Mnist

• Kaggle

• Jhon Hopkin's public dataset of Covid 19

What are the properties of healthy x-ray image in the dataset?

• Clear lungs without any areas of abnormal cloudiness

• Clear lungs with some areas of opacity

• Presence of diaphragm shadow

• Absence of diaphragm shadow

What are the properties of pneumonia images in dataset?

• Clear lungs without any areas of abnormal cloudiness/opacity

• Areas of cloudiness/opacity in several concentrated areas or one large area.

• General pattern of opacity that obscures the structure of the lungs, heart and diaphragm.

• Diaphragm shadow is not obscured

As a task you will need to submit an HTML file of a complete job Preview, which includes________

• XML code files

• Example test questions

• Instructions for annotation

• Leveled images

What you should include in case of uncertainty in an annotation?

• Serious case

• Unknown

• Healthy case

• Other

Is this project Multi-class multi-label classification?

• Yes

• No

In case of pneumonia, how you can annotate?

• On a numerical scale

• 0-n for their confidence that an image contains pneumonia symptoms or not.

• String value like Pneumonia / Not-pneumonia

• By color i.e. red for Pneumonia and Green for Not-pneumonia

Of over 100 images in this dataset _________

• All of them are labeled

• Very few of them are labeled

• All of them are unlabeled

• Most of them are labeled