## 3 Methodology
### Proposed CNN Model Architecture
#### 3.4.1 Classification Module (Anemic or Healthy)  
In the classification module, we fused [64; 69; 70] in 3-tiers to attain optimized training feature selection with unbiased validation after the execution of each tier. There are \(n(n+1)/2\) direct connections for N networks. The feature maps of all preceding networks are used as inputs and their own feature map of the current network for each subsequent network, as shown in Figure 8. At tier-I, DenseNet [64] is implemented, which helps to examine the RBC elements at the pixel level. At tier-II, Efficient-Net [70] is applied, which received the feature map of the preceding network as an input along with original input. Tier-II preserves the pixel-wise semantic information for analyzing morphological deformities in blood images to detect healthy and anemic-RBC elements. Efficient-net is 18 times faster than existing models that require 75 times less floating-point operation per second and 79 times fewer parameters, while during tier-III, ShuffleNet [69] maintains comparable accuracy with the least computational power. The primary aim of using fused three-tier architecture is to simultaneously examine the input image with densely connected networks \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Image Type**} & \multirow{2}{*}{**Healthy Images**} & \multicolumn{3}{c}{**Anemic Images**} & \multicolumn{2}{c}{**Total**} \\ \cline{3-6}  & & **Mild** & **Chronic** & **Healthy + Anemic** & **Original + Segmented** \\ \hline Original Images & 5750 & 2875 & 2875 & 11,500 & & \\ \hline Manual Segmented Images & 5750 & 2875 & 2875 & 11,500 & 23,000 \\ \hline \multirow{2}{*}{Training Images} & Original & 4025 & 2012 & 2013 & 8050 & \\  & Segmented & 4025 & 2012 & 2013 & 8050 & 11,500 \\ \hline \multirow{2}{*}{Validation Images} & Original & 575 & 287 & 288 & 1150 & \\  & Segmented & 575 & 287 & 288 & 1150 & 2300 \\ \hline \multirow{2}{*}{Test Images} & Original & 1150 & 575 & 575 & 2300 & \\  & Segmented & 1150 & 575 & 575 & 2300 & 4600 \\ \hline \multirow{2}{*}{Total RBC Elements} & Original & 375,000 & 187,500 & 187,500 & \textasci{{}_{-}}750,000 & \\  & Segmented & 375,000 & 187,500 & 187,500 & \textasci{{}_{-}}750,000 & 1,500,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detailed description of original and segmented images used during the training, validation, and testing of the proposed model. to preserve pixel-level features and semantic information. The extracted morphological features are then used to identify the severity level of the anemia. The 3-TierDCFNet received a pre-processed original image (Oo) with \(300\,\times\,300\,\times\,3\) size input to the tier-I with a manually generated ground truth image (Og). The Og image is used for validation purposes. A threshold (\(\bar{\text{T}}\)) value is set as condition (\(\bar{\text{C}}\)) for the validation (\(V\)o) purpose of each tier's output. The constant weight of \(\bar{\text{T}}\) is set as \(55\%\), \(70\%\), and \(90\%\) for the first, second, and third tiers, respectively. At the end of each tier training, a validation process is initiated that validates the output. Suppose that \(V\)o got a value greater than the predefined \(\bar{\text{T}}\) value of the \(n^{\text{th}}\) tier. The output feature map (\(\hat{O}\)i) is given as an input to the \(n+1\) Tier. The \(\hat{O}\)i of \(n^{\text{th}}\) tier includes morphological features and pixel-level and semantic information extracted by the \(n^{\text{th}}\) tier model. On the other hand, if the \(n^{\text{th}}\) tier got a \(V\)o value less than predefined \(\bar{\text{T}}\), then the \(\hat{O}\)i of the \(n^{\text{th}}\) tier model is given as an input again to the same tier for more optimal features selection. Mathematically, the whole training and validation process is defined as follows:  
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