This article explores the use of machine learning in the early detection of brain haemorrhage types, leveraging a large anonymized dataset of brain scan images. Discover how machine learning can support the identification of various haemorrhage types before human review, enhancing the efficiency and accuracy of diagnosis.
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?
The correct answer is C. classification.
Classification is a type of supervised machine learning that is used to predict categorical values, such as labels, classes, categories, etc. Classification models learn the relationship between input features and a discrete target variable, and then use this relationship to assign a class to new data.
In this scenario, the task is to support early detection of the different brain haemorrhage types in the images before the images are reviewed by a person. The brain haemorrhage types are predefined categories or classes, such as intracerebral, subarachnoid, subdural, etc. The input features are the brain scan images, and the target variable is the brain haemorrhage type. Therefore, classification is the most suitable type of machine learning to use.
The other options are not correct for the following reasons:
- Clustering: This is a type of unsupervised machine learning that is used to discover groups of similar data points, without using any labels or target variables. Clustering models learn the structure and patterns in the data, and then use this structure to assign a cluster to new data. This is not applicable in this scenario, as the brain haemorrhage types are already known and labeled.
- Regression: This is a type of supervised machine learning that is used to predict numeric values, such as sea level, temperature, sales, etc. Regression models learn the relationship between input features and a continuous target variable, and then use this relationship to make predictions on new data. This is not applicable in this scenario, as the brain haemorrhage type is not a numeric value, but a categorical value.
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