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Answer: Use a complex model architecture, label with class labels, evaluate using precision and recall.
For critical applications like healthcare, a complex model architecture is often necessary to achieve high accuracy. Labeling with class labels is appropriate for image classification tasks. Evaluating using precision and recall is crucial as it provides a balanced view of the model's performance, especially important in scenarios where false positives and false negatives have significant consequences.
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You are developing a custom image classification model for a healthcare application that needs to classify X-ray images into different categories. The model needs to be highly accurate and reliable due to the critical nature of the application. Describe how you would approach the training of this model, including the choice of model architecture, the labeling process, and the metrics you would use to evaluate the model's performance.
A
Use a lightweight model architecture, label with object tags, evaluate using accuracy.
B
Use a complex model architecture, label with bounding boxes, evaluate using mean average precision.
C
Use a complex model architecture, label with class labels, evaluate using precision and recall.
D
Use a lightweight model architecture, label with bounding boxes, evaluate using F1 score.
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