## 3 Methodology
### Proposed CNN Model Architecture
#### 3.4.2 Anemia Severity Detection Module  
When an input image is classified as anemic, the anemia severity detection module is activated to detect the severity level of disease in an image. The Module-II classification in  
Figure 8: Shows the dense-connection among the tiers of the network. Here covered lines represent the feature map of each tier. These connections illustrate that the feature maps of all preceding networks are used as input to each subsequent network.  
Figure 7: Synaptic view of proposed three-Tier CNN model. The 300 \(\times\) 300 \(\times\) 1 represents binary input, while 300 \(\times\) 300 \(\times\) 3 represents RGB original image. The label “Yes” means the training accuracy of the \(n^{\text{th}}\) Tier is equal to the predefined threshold value. The output is then given to the next tier for further processing. The label “No” means the training accuracy of the \(n^{\text{th}}\) tier is not equal to the predefined threshold value. The output is then given to the same tier for more optimal feature selection. Tier-I includes the DenseNet model that receives inputted images of 300 \(\times\) 300 \(\times\) 3 and produces an output of 200 \(\times\) 200 \(\times\) 64. Tier-II is equipped with EfficeintNet to preserve semantic information and extract the features up to 100 \(\times\) 100 \(\times\) 128; tier-III comprises ShuffleNet, which ensures high accuracy with less computational cost.  
Figure 7 shows the anemia detection architecture for anemia-positive images. The severity of anemic RBC was determined using morphological features like shape and size. This study categorizes anemic images into two levels, i.e., mild and chronic. To point out healthy, mild, and chronic morphological features of RBC, the proposed dataset is divided into three classes along with manually annotated labels. Figure 3 shows the annotated healthy, mild, and chronic RBC elements. A scoring mechanism was used for the severity assessment of anemic RBC elements. Three parameters were considered for setting the score range of the severity stages: (1) RBC size, (2) RBC shape, and (3) central white pallor size. Table 3 shows the score range of each parameter. The RBC image dataset was prepared using an Olympus Dp27 at a 40\(\times\) magnification rate for experimental purposes. The magnified size of RBC elements in centimeters (cm) is described in Table 3. Due to the greater intensity of RBC elements in blood, sometimes it shows an overlapped structure that leads to the inappropriate counting of RBC elements using a simple microscope. Euclidean Distance Transform (EDT) [71] was applied to overcome this issue. Before EDT transformation, the images' background was removed manually. This transformation works on the pixel-level marking of the binary RBC element. The threshold for marking is set as 70. We find the pixel intensity of RBC elements in overlapped areas using this technique. When pixel intensity goes beyond the threshold value, it marks the pixels and separates overlapped areas.  
The severity detection Algorithm 1 was applied in Module-II classification to determine the severity of the anemia in the RBC element. This algorithm classifies the anemic images into mild or chronic using the scoring parameters described in Table 3.