## 5 Result and Discussion
Color and borders are taught by the initial layer of the CNN framework, whereas more intricate properties like tumor/feature borders are learned by the CNN framework's convolution layers. The features of subsequent convolution layers are produced by combining the information learned by preceding convolution layers. The early layer of CNN for the classification duty has 128 channels into 96. The forward convolution layer, which includes 96 channels, which are 2D tensors, makes up each layer of CNN. Pixels whose value is near 255 have a lot of positives, whereas pixels whose value is near 0 have negative activations in these images. In the same way, gray pixels in the input image represent weakly active channels. Without ever being asked to learn about \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Batch Size**} \\ \hline \multirow{4}{*}{**Kernel**} & \multirow{2}{*}{**3**} & **8** & **16** & **32** & **64** \\ \cline{3-6}  & & 0.88/13.6 & 0.86/13.4 & **0.95/13.3** & 0.89/13.7 \\ \cline{2-6}  & & 0.86/15.3 & 0.82/14.9 & **0.88/15.9** & 0.86/15.7 \\ \cline{2-6}  & **7** & 0.86/17.5 & 0.84/17.7 & **0.87/17.3** & 0.85/18.2 \\ \cline{2-6}  & **9** & 0.82/18.1 & 0.81/18.5 & **0.83/17.8** & 0.80/18.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Kernel Value vs. Batch Size for Binary Classification: CNN model “Deep-Hist” for training with hyperparameters such as Batch Size and Kernel Values optimization using the proposed algorithm (AHT) and achieved plausible results with a Batch Size of 32 and Kernel Value as 3. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Activation Function**} \\ \hline \multirow{4}{*}{**Kernel**} & \multirow{2}{*}{**3**} & **relu** & **leakyrelu** & **swish** & **mish** \\ \cline{3-6}  & & 0.89/12.9 & 0.87/13.2 & **0.95/12.7** & 0.90/13.4 \\ \cline{2-6}  & & 0.87/15.7 & 0.85/14.6 & **0.89/14.3** & 0.87/14.3 \\ \cline{2-6}  & **7** & 0.88/17.3 & 0.83/17.5 & **0.88/26.7** & 0.87/16.9 \\ \cline{2-6}  & **9** & 0.85/18.3 & 0.83/18.1 & **0.85/27.3** & 0.83/17.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Kernel Value vs. Activation Functions (AF) for Binary Classification: CNN model “Deep-Hist” for training with hyperparameters such as Activation Functions (relu, leakyRelu, swish, mish) and Kernel Values optimization using the proposed algorithm (AHT) and achieved plausible results with activation function as swish and Kernel Value as 3.  
malignancy, it is plausible to assume that CNN has discovered that malignant features are distinguishing characteristics that can be used to differentiate across binary classes. Unlike previous CNN models, which were often created to be problem-specific, this convolution neural network model may learn important properties on its own. In this article, learning to detect malignant and benign aids in the differentiation of a malignant image from a benign image. ```
AHT \(F\) tuning function \(\lambda_{i}\) unified distribution of hyperparameters, \(\Omega=\lambda_{0}\), \(\lambda_{1}\), \(\lambda_{2}\),..., \(\lambda_{N}\). Requires: Evaluate the real objective cost function O(F\({}^{\prime}\)) for random hyperparameter points F\({}^{\prime}\) in the hyperparameter space. while\(F^{\prime}\
eq 0\)do if\(\lambda^{\prime}\) is \(\in\mathbb{C}^{\mathcal{S}}\)then  Create a surrogate model (Tree Parzen Estimator model) to estimate the genuine unbiased cost function using the specified hyperparameter (F\({}^{\prime}\)) variables and recorded events.