## 5 Result and Discussion
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Sensitivity** & **Specificity** & **Precision** & **Accuracy** & **F1-Score** &
\begin{tabular}{c} **Trainable** \\ **Parameter** \\ \end{tabular} \\ \hline ResNet & 83.43 & 84.57 & 87.17 & 87.26 & 86.39 & \textgreater{}58.5 M \\ \hline DenseNet & 86.79 & 86.97 & 87.93 & 87.59 & 88.74 & \textgreater{}12.8 M \\ \hline MobileNetV2 & 84.97 & 85.13 & 84.57 & 84.18 & 85.13 & \textgreater{}0.44 M \\ \hline
**Deep-Hist** & **93.11** & **93.51** & **94.71** & **95.71** & **93.57** & \textless{}0.27M \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Training output of Accuracy, F1-Score, Specificity, Precision and Sensitivity on combine datasets. \begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Dataset** & **Model** & **Sensitivity** & **Specificity** & **Precision** & **Accuracy** & **F1-Score** \\ \hline \multirow{4}{*}{**BraTS**} & ResNet & 79.13 & 79.66 & 80.23 & 80.35 & 79.84 \\ \cline{2-6}  & DenseNet & 80.19 & 80.35 & 80.76 & 80.59 & 80.74 \\ \cline{2-6}  & MobileNetV2 & 81.22 & 80.32 & 80.74 & 80.25 & 81.41 \\ \cline{2-6}  & **Deep-Hist** & **91.38** & **90.42** & **90.49** & **91.08** & **90.84** \\ \hline \multirow{4}{*}{**BreakHis**} & ResNet & 78.29 & 78.71 & 79.06 & 79.01 & 79.15 \\ \cline{2-6}  & DenseNet & 79.65 & 80.05 & 80.12 & 80.24 & 79.85 \\ \cline{2-6}  & MobileNetV2 & 80.27 & 80.91 & 80.63 & 80.27 & 81.07 \\ \cline{2-6}  & **Deep-Hist** & **90.37** & **91.14** & **91.09** & **91.26** & **91.37** \\ \hline \multirow{4}{*}{**NIH X-ray**} & ResNet & 82.11 & 82.67 & 82.96 & 82.78 & 83.02 \\ \cline{2-6}  & DenseNet & 84.36 & 84.01 & 84.36 & 84.25 & 84.39 \\ \cline{1-1} \cline{2-6}  & MobileNetV2 & 83.89 & 82.85 & 83.74 & 83.97 & 84.08 \\ \cline{1-1} \cline{2-6}  & **Deep-Hist** & **92.28** & **92.71** & **92.69** & **93.21** & **92.83** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Training Accuracy, F1-Score, Specificity, Precision and Sensitivity on different datasets such as BraTS, BreakHis, NIH X-ray. The experimental results and statistical analysis present that while a Random Search (RS) may be effective for a small dataset, it is insufficient for a medium-scale dataset. Due to the intrinsic unpredictability of the acquisition procedure, RS surpasses Grid Search (GS) and Bayesian Optimization (BO) in some circumstances. In comparison, the proposed hyperparameter optimization technique (AHT) is disciplined, with a model-based approach and theoretically sound hyperparameter adjustment. As a result, it is a good choice for large-sized datasets and complicated patterns. The empirical results and statistical analysis further show that the number of hyperparameters to be adjusted, the size of the dataset, and the Imbalance ratio all influence the choice of the HPO method for CNN models. The findings of this study's trials show that, when compared to Grid Search and Random Search, AHT has the ability to improve classifier demonstration in many circumstances since AHT picks the next hyperparameters with care. As a result, for many of the datasets utilized in this work, it is a superior choice for non-trivial hyperparameter search spaces. Experiments demonstrate that the Deep-Hist CNN architecture along with the proposed AHT optimization algorithm (Deep-Hist-AHT) may efficiently respond to BreakHis, BraTS, and Covid-19 datasets. Furthermore, the Deep-Hist demonstrates great classification accuracy and F1-Score with acceptable spatial characterization on different pairs of healthcare datasets and outperforms the ResNet, DenseNet, and MobileNetV2 in terms of accuracy, Specificity, Sensitivity, and especially in learnable parameters. The variety in findings shows that simply adjusting the learning rate, activation function and dropout could not be enough to tailor a CNN architecture to different datasets. Furthermore, the collection of optimal hyperparameters we chose for CNN learning framework to adapt and enables the proposed model to suit fresh datasets efficiently.