how to handle the imbalance class

support,  our task is to identify rare lung infection and 99% of images are of normal lung. then even we get 99% accuracy that still been useless.

Even though we get 99% accuracy, how to identify, we have achieved our goal or not.

The confusion matrix comes to the rescue. by this, we can find out, out of how many actual points, rare class predicted correctly.

But here the question is how to identify this when the model is training, typically binary or categorical cross-entropy ensures that when misclassification is high, the loss is also very high.

however, we can assign a higher weight to rarely occurring classes. 


2. we can also use image augmentation, we can oversample the rare class image by increasing their mix with the overall population.

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