Learn which statement about AI diagnosing diabetic retinopathy is false. Understand how labeled retina images, feature extraction, and unseen data impact AI training.
Table of Contents
Question
Which of the following statements is NOT true regarding the diagnosis of Diabetic Retinopathy by AI?
A. Multiple labeled images of healthy and diseased retinas are provided to the AI as input data
B. Different types of filters are provided as rules to the AI
C. During training, the AI determines the best possible filters to extract key features from the images
D. After training, the AI is provided images of previously unseen retinas and tries to label them appropriately
Answer
The statement that is NOT true regarding the diagnosis of Diabetic Retinopathy by AI is:
B. Different types of filters are provided as rules to the AI
Explanation
In the process of training an AI model to diagnose Diabetic Retinopathy:
- Multiple labeled images of both healthy and diseased retinas are provided to the AI as input data. This allows the AI to learn the distinguishing features of retinas with and without the condition.
- During training, the AI itself determines the best possible filters to extract key features from the images. The AI learns these filters through the training process, rather than being explicitly provided with different types of filters as rules.
- After the AI has been trained, it is tested by being provided with images of previously unseen retinas. The AI then attempts to appropriately label these new images as either showing signs of Diabetic Retinopathy or being healthy, based on the features it learned to recognize during training.
So while labeled data, feature extraction, and testing on unseen data are all crucial parts of training an AI to diagnose Diabetic Retinopathy, explicitly providing the AI with different types of filters as rules is not part of the process. The AI learns the appropriate filters itself during training.
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