Machine Learning for Early Detection of Brain Haemorrhage Types
Question
A medical research project uses a large anonymized dataset of brain scan images that are categorized into predefined brain haemorrhage types.
You need to use machine learning to support early detection of the different brain haemorrhage types in the images before the images are reviewed by a person.
This is an example of which type of machine learning?
Answers
Explanations
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A. B. C.C
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/introductionThe medical research project described in the question involves using machine learning to support early detection of different brain hemorrhage types in anonymized brain scan images. In machine learning, there are different types of problems that can be solved, such as clustering, regression, and classification.
Clustering involves grouping data points into clusters based on similarities, without any predefined labels or categories. In this case, the brain scan images are already categorized into predefined brain hemorrhage types, so clustering is not the appropriate type of machine learning to use.
Regression involves predicting a continuous numerical output based on input features. For example, given the age and weight of a patient, we can use regression to predict their blood pressure. However, in this case, the output we are trying to predict is not a continuous numerical value but rather a categorical label indicating the type of brain hemorrhage present in the image. Therefore, regression is also not the appropriate type of machine learning to use.
Classification involves predicting a categorical label based on input features. In this case, we want to predict the type of brain hemorrhage present in the brain scan image, which is a categorical label. Therefore, the appropriate type of machine learning to use is classification.
To summarize, the medical research project described in the question is an example of classification machine learning, as the goal is to predict the type of brain hemorrhage present in the brain scan images, which is a categorical label.
