# Azure-Based Smart Monitoring System for Anemia-Like Pallor
## 3 System Architecture Overview
### Back-End Methodology
#### 3.4.3 Classification  
The final step in data the models \(M1\) and \(M2\) is classification. For analytical purposes two categories of classifiers are analyzed that vary in their computational and parametrization complexities. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **\# Features** & **Meaning** \\ \hline Pallor Site: & Eye & \\ \hline Color Planes in \(g_{conj}\) & (6 \(\times\) 3) = 18 & Max, mean, variance of pixels in red, green, blue, hue, saturation, intensity color planes. \\ \hline Gradient intensity in & & \\ \(I_{green}^{G}\) for \(R_{conj}\) & 5 & Max, min, mean, variance and intensity of pixels. \\ \hline Frangi-filtered intensity in & & \\ \(I_{green}^{F}\) for \(R_{conj}\) & 4 & Max, mean, variance and intensity of pixels. \\ \hline Color Planes in \(g_{sclera}\) & (6 \(\times\) 3) = 18 & Max, mean, variance of pixels in red, green, blue, hue, saturation, intensity color planes. \\ \hline Gradient intensity in & & \\ \(I_{green}^{G}\) for \(R_{sclera}\) & 5 & Max, min, mean, variance and intensity of pixels. \\ \hline Frangi-filtered intensity in & & \\ \(I_{green}^{F}\) for \(R_{sclera}\) & 4 & Max, mean, variance and intensity of pixels. \\ \hline Pallor Site: & Tongue & \\ \hline Color Planes in \(g_{outer}\) & (6 \(\times\) 3) = 18 & Max, mean, variance of pixels in red, green, blue, hue, saturation, intensity color planes. \\ \hline Gradient intensity in & & \\ \(I_{green}^{G}\) for \(R_{outer}\) & 5 & Max, min, mean, variance and intensity of pixels. \\ \hline Frangi-filtered intensity in & & \\ \(I_{green}^{F}\) for \(R_{outer}\) & 4 & Max, mean, variance and intensity of pixels. \\ \hline Color Planes in \(g_{inner}\) & (6 \(\times\) 3) = 18 & Max, mean, variance of pixels in red, green, blue, hue, saturation, intensity color planes. \\ \hline Gradient intensity in & & \\ \(I_{green}^{G}\) for \(R_{inner}\) & 5 & Max, min, mean, variance and intensity of pixels. \\ \hline Frangi-filtered intensity in & & \\ \(I_{green}^{F}\) for \(R_{inner}\) & 4 & Max, mean, variance and intensity of pixels. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Pixel-intensity Feature Extraction for model \(M1\). The first category represents non-linear multi-class classifiers with low computational complexity, such as, k-nearest neighbors (kNN) [26]. Compared to our prior work in [4], the kNN classifiers are preferred due to their ease in implementation and generalizability across pallor site images when compared to decision trees data models [26]. The second classifier category involves involves parametrization of several hidden layers in a CNN model using the training data set. These classification data models are described below. 1. kNN : The training data set is used to populate the feature space, followed by identification of '\(K\) nearest neighbors to each test sample, and identification of the majority class label [26]. In the training phase, the optimal value of '\(k\)' that minimizes validation error is searched in the range [3:25], in steps of 2. For model \(M1\), corresponding to each image \(I\), 54 region-based features are extracted, which represent the feature space, followed by determination of pallor class label for each test image. Due to low computational complexity of this classifier, the final \(M1\), \(M2\) models are deployed for pallor classification using the kNN classifier. 2. CNN: This category of classifiers, with high computational complexity, is motivated by the prior works in [5,6]. Here, each input image is subjected to several hidden layers of feature learning to generate an output vector of probability scores, for each image to belong to an output class label. For this analysis, we implement CNN architecture with the following 7 hidden layers: convolutional (C)-subsampling (S)-activation (A)-convolutional (C)-subsampling (S)-activation (A)-neural network (NN).