  Some real-world domains, such as Agriculture and Healthcare, comprise
early-stage disease indications whose recording constitutes a rare event, and
yet, whose precise detection at that stage is critical. In this type of highly
imbalanced classification problems, which encompass complex features, deep
learning (DL) is much needed because of its strong detection capabilities. At
the same time, DL is observed in practice to favor majority over minority
classes and consequently suffer from inaccurate detection of the targeted
early-stage indications. To simulate such scenarios, we artificially generate
skewness (99% vs. 1%) for certain plant types out of the PlantVillage dataset
as a basis for classification of scarce visual cues through transfer learning.
By randomly and unevenly picking healthy and unhealthy samples from certain
plant types to form a training set, we consider a base experiment as
fine-tuning ResNet34 and VGG19 architectures and then testing the model
performance on a balanced dataset of healthy and unhealthy images. We
empirically observe that the initial F1 test score jumps from 0.29 to 0.95 for
the minority class upon adding a final Batch Normalization (BN) layer just
before the output layer in VGG19. We demonstrate that utilizing an additional
BN layer before the output layer in modern CNN architectures has a considerable
impact in terms of minimizing the training time and testing error for minority
classes in highly imbalanced data sets. Moreover, when the final BN is
employed, minimizing the loss function may not be the best way to assure a high
F1 test score for minority classes in such problems. That is, the network might
perform better even if it is not confident enough while making a prediction;
leading to another discussion about why softmax output is not a good
uncertainty measure for DL models.
