  Paleness or pallor is a manifestation of blood loss or low hemoglobin
concentrations in the human blood that can be caused by pathologies such as
anemia. This work presents the first automated screening system that utilizes
pallor site images, segments, and extracts color and intensity-based features
for multi-class classification of patients with high pallor due to anemia-like
pathologies, normal patients and patients with other abnormalities. This work
analyzes the pallor sites of conjunctiva and tongue for anemia screening
purposes. First, for the eye pallor site images, the sclera and conjunctiva
regions are automatically segmented for regions of interest. Similarly, for the
tongue pallor site images, the inner and outer tongue regions are segmented.
Then, color-plane based feature extraction is performed followed by machine
learning algorithms for feature reduction and image level classification for
anemia. In this work, a suite of classification algorithms image-level
classifications for normal (class 0), pallor (class 1) and other abnormalities
(class 2). The proposed method achieves 86% accuracy, 85% precision and 67%
recall in eye pallor site images and 98.2% accuracy and precision with 100%
recall in tongue pallor site images for classification of images with pallor.
The proposed pallor screening system can be further fine-tuned to detect the
severity of anemia-like pathologies using controlled set of local images that
can then be used for future benchmarking purposes.
