# Azure-Based Smart Monitoring System for Anemia-Like Pallor
## 5 Conclusions and Discussion  
The proposed work presents a novel hardware independent application that is optimized in design for fast end-to-end-communication using computationally less-intensive region-based features and generalizable data models for image-based pallor classification. We perform quantitative and qualitative assessment of eye and tongue pallor site images with varying pallor severities to design two application models with a shared front-end that can be incorporated for personalized pallor detection and pallor screening tasks, respectively. We perform three categories of experiments on the pallor site images that are acquired from the public domain. The first experiment demonstrates that proposed application font-end has significantly lesser computational time complexity (order of \(10^{-5}\)) when compared to the back-end processing times. The second experiment detects the most discriminating color and gradient plane-transformed images that are significant for classification of image-based pallor. This experiment qualitatively demonstrates that Frangi-filtered hue and saturation color planes and first-order gradient filtered luminance channel planes are most significant for pallor classification using eye and tongue images, respectively. In the third quantitative experiment, we observe that pallor classification, using the two proposed data models, results in 86% screening accuracy for eye images while color-transformations and gradient filtering leads to 98% screening accuracy for tongue images. Thus, the proposed applications are capable of severity screening for anemia using facial pallor site images in under 20 seconds of computation time per image. Three significant conclusions can be drawn from our analysis. First, for the eye pallor site images, model \(M1\) that involves spatial segmentation of each image followed by intensity-based feature extraction and classification is the best model for separating normal images from abnormal ones (class 0 vs. 1,2). For the tongue data set, however, model \(M2\) that involves color and gradient transformations followed by classification is better for overall anemia screening tasks. Second, the CNN model over-fits to predict the class label with maximum number of class labels. For instance, in the eye data set with model \(M1,CNN\), classification of class 0 vs. 1,2 achieves \(RE=1\) due to the class samples = \([6,12]\) for the binary classification task. Thus, all test samples are classified as class 1 or 2 by the CNN data model, or over-prediction. Also, for the tongue data set with model \(M1,CNN\), all test samples are classified to the majority class labels, thereby resulting in \(RE=1\). On the other hand, kNN is more resilient to sample class imbalance. Third, the eye pallor site images are better for anemia-like pallor screening when compared to the tongue pallor site images since the combined AUC under the hierarchical classification tasks is greater for the eye pallor site classification tasks. This observation is in line with the clinical analysis for beta thalassemia in [14]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & \(M1\)**, kNN** & \(M2\)**, kNN** & \(M1\)**, CNN** & \(M2\)**, CNN** \\ \hline Task & 0/1,2 & 1/2 & 0/1,2 & 1/2 & 1/0,2 & 0/2 & 2/0,1 & 0/1 \\ \hline PR  
With the two proposed hardware-independent non-invasive medical diagnostics screening platforms, users will be able to dictate their use based on preferences for speed versus accuracy in screening results. The application with \(M1\) back-end, which provides more accurate pallor screening at slower processing speeds due to ROI segmentation requirements, is optimal for longitudinal assessment of personalized pallor. However, the application with the \(M2\) back-end, which provides slightly less accurate results at faster processing speeds, becomes preferable for large-scale screening applications. Future works can be directed towards extracting pallor-specific bio-markers from patient skin-color based features.