  The antinuclear antibody detection with human epithelial cells is a popular
approach for autoimmune diseases diagnosis. The manual evaluation demands time,
effort and capital, and automation in screening can greatly aid the physicians
in these respects. In this work, we employ simple, efficient and visually more
interpretable, class-specific features which defined based on the visual
characteristics of each class. We believe that defining features with a good
visual interpretation, is indeed important in a scenario, where such an
approach is used in an interactive CAD system for pathologists. Considering
that problem consists of few classes, and our rather simplistic feature
definitions, frameworks can be structured as hierarchies of various binary
classifiers. These variants include frameworks which are earlier explored and
some which are not explored for this task. We perform various experiments which
include traditional texture features and demonstrate the effectiveness of
class-specific features in various frameworks. We make insightful comparisons
between different types of classification frameworks given their silent aspects
and pros and cons over each other. We also demonstrate an experiment with only
intermediates samples for testing. The proposed work yields encouraging results
with respect to the state-of-the-art and highlights the role of class-specific
features in different classification frameworks.
