## 3 Methodology
### Classification
#### 3.5.8 Softmax Layer  
Softmax is a numerical capacity that changes over a vector of numbers into a vector of probabilities, where the probabilities of each value correspond to the general size of each value in the vector. Convolutional layers are layers where channels are applied to the first picture, or to other element maps in a deep CNN. We use 32 layers for training. The table of CNN layers (Table 3) is given below. Figure 3: Proposed CNN design with convolve layer weight activation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Layer Names** & **Activations** & **Kernel Size** & **Stride** & **Parameters** & **Feature Maps** \\ \hline
1 & Input layer & 1024 \(\times\) 1024 \(\times\) 3 & / & 1024 \(\times\) 1024 & / & / \\ \hline
2 & Convolutional layer (C1) & 1024 \(\times\) 1024 \(\times\) 3 & 3 \(\times\) 3 & 1 & Weights = 3 \(\times\) 3 \(\times\) 3 \(\times\) 16 & 16 \\ \hline
3 & Batch Normalization (BN1) & 1024 \(\times\) 1024 \(\times\) 16 & / & / & Offset = 1 \(\times\) 1 \(\times\) 16 & 16 \\ \hline
4 & ReLU (R1) & 1024 \(\times\) 1024 \(\times\) 16 & / & / & / & / \\ \hline
5 & Maximum pooling layer (MP1) & 1024 \(\times\) 1024 \(\times\) 16 & 2 \(\times\) 2 & 2 & / & / \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Details about the proposed CNN layers. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number** & **Layer Names** & **Activations** & **Kernel Size** & **Stride** & **Parameters** & **Feature Maps** \\ \hline
6 & Convolutional layer (C2) & \(512\times 512\times 16\) & \(3\times 3\) & \(1\) & Weights = \(3\times 3\times 16\times 32\) & 32 \\ \hline
7 & Batch Normalization (BN2) & \(512\times 512\times 32\) & / & / & Offset = \(1\times 1\times 32\) & 32 \\ \hline
8 & ReLU (R2) & \(512\times 512\times 32\) & / & / & / & / \\ \hline
9 & Maximum pooling layer (MP2) & \(256\times 256\times 32\) & \(2\times 2\) & 2 & / & / \\ \hline
10 & Convolutional layer (C3) & \(256\times 256\times 64\) & \(3\times 3\) & \(1\) & Weights = \(3\times 3\times 32\times 64\) & 64 \\ \hline
11 & Batch Normalization (BN3) & \(256\times 256\times 64\) & / & / & Offset = \(1\times 1\times 32\) & 64 \\ \hline
12 & ReLU (R3) & \(256\times 256\times 64\) & / & / & / & / \\ \hline
13 & Maximum pooling layer (MP3) & \(128\times 128\times 64\) & \(2\times 2\) & 2 & / & / \\ \hline
14 & Convolutional layer (C4) & \(128\times 128\times 128\) & 3 \(\times\) 3 & \(1\) & Weights = \(3\times 3\times 64\times\) & 128 \\ \hline
15 & Batch Normalization (BN4) & \(128\times 128\times 128\) & / & / & Offset = \(1\times 1\times 128\) & 128 \\ \hline
16 & ReLU (R4) & \(128\times 128\times 128\) & / & / & / & / \\ \hline
17 & Maximum pooling layer (MP5) & \(64\times 64\times 128\) & \(2\times 2\) & 2 & / & / \\ \hline
18 & Convolutional layer (C5) & \(64\times 64\times 256\) & \(3\times 3\) & \(1\) & Weights = \(3\times 3\times 128\times\) & 256 \\ \hline
19 & Batch Normalization (BN5) & \(64\times 64\times 256\) & / & / & Offset = \(1\times 1\times 256\) & 256 \\ \hline
20 & ReLU (R5) & \(64\times 64\times 256\) & / & / & / & / \\ \hline
21 & Maximum pooling layer (MP5) & \(32\times 32\times 256\) & \(2\times 2\) & 2 & / & / \\ \hline
22 & Convolutional layer (C6) & \(32\times 32\times 512\) & \(3\times 3\) & \(1\) & Weights = \(3\times 3\times 256\times\) & 512 \\ \hline
23 & Batch Normalization (BN6) & \(32\times 32\times 512\) & / & / & Offset = \(1\times 1\times 512\) & 512 \\ \hline
24 & ReLU (R6) & \(32\times 32\times 512\) & / & / & / & / \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._Table 3 shows the fine-tuned CNN details for 15 categories of chest disease detection, where padding, stride, and other fine details of CNN show how the fine features are being collected and passed on to the next block. There are seven total Conv-Blocks in which a 4-layer combination is used by conducting a downsample of half of the size, as compared to the previous layer input.