## 5 Result and Discussion  
The models (pretrained models such as ResNet, DenseNet, MobileNetV2, and our customized model Deep-Hist which is used for the diagnosis of cancer on Breast Histopathological images [7]) were first trained with a learning rate of [0.01, 0.001, 0.0001 and 0.00001], epoch size are [20, 40, 60, 80], batch size of [8, 16, 32, 64] and kernel/filter size of [3, 5, 7, 9]. The models are trained on a variety of datasets such as COVID-19 Xray [10], BreakHis [8], and Brain Tumor Segmentation (BraTS) Challenge in 2020 and 2021 and obtained the maximum baseline accuracy of 95.71 percent depicted in Tables 1-3. Tables 1-3 depicts the AHT optimal set of hyperparameters using the surrogate model and a combination of the Gaussian process and Tree Parzen Estimator methodologies. It can be seen that the hyperparameters used with the presented AHT optimization model, such as kernel sizes and activation functions are identical. The major distinction is determined by the set of the kernel and the selection of the learning rate. Thus, the decrease in the number of learnable parameters is the significant enhancement of our method over the other Bayesian Optimization approach. The AHT optimization algorithm managed to construct a CNN model that was 79% lighter than the ResNet, 61% lighter than the DenseNet, and 44% less than the capacity of the MobileNetV2. Our proposed algorithm is offering muchly improved or comparable classification performance. The reduction in the number of learnable parameters reduces the time for training, forecasting time, and the demand for processing capacity significantly. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Learning Rate**} \\ \hline  & & **0.01** & **0.001** & **0.0001** & **0.00001** \\ \cline{3-6}  & **3** & 0.87/13.8 & 0.88/13.6 & **0.95/13.3** & 0.86/14.1 \\ \cline{2-6}
**Kernel** & **5** & 0.83/15.3 & 0.86/14.9 & 0.84/15.7 & **0.88/15.9** \\ \cline{2-6}  & **7** & 0.86/17.5 & 0.84/17.7 & **0.87/17.3** & 0.85/18.2 \\ \cline{2-6}  & **9** & 0.81/18.5 & 0.82/18.1 & 0.80/18.4 & **0.83/17.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Kernel Value vs. Learning Rate for Binary Classification: CNN model “Deep-Hist” for training with hyperparameter optimization using the proposed algorithm (AHT) and achieved plausible results with a kernel value of 3 and a learning rate of 0.0001. In this study, Algorithm 1 is initially used to optimize architectural hyperparameters. After the basic hyperparameters have been determined, Algorithm 1 is used to fine-tune the elegant adjustment hyperparameters. The proposed method is applied to the training set using a 5-fold cross-validation strategy in this suggested study. The provided datasets are divided into five sections, four of which are utilized for training and the fifth for testing. There are 23,950 images in the classification job, which are randomly divided into training, validation, and test sets in an 80:10:10 ratio. The proposed method, in essence, assesses the optimal hyperparameter value combinations and returns the one with the highest accuracy. To obtain the best accuracy in Algorithm 1, four hyperparameters must be modified. Combining these hyperparameter values can be conducted in various ways, including 4 values of the learning rate, four values of Kernel, four values of activation function, and four of batch size. For the binary classification job, the performance of the recommended models such as ResNet, DenseNet, MobileNetV2, and Deep-Hist are evaluated using a 5-fold cross-validation technique. The combined data set is divided into five sections, with four used for training and the fifth for testing/validation. The experiments are carried out five times. After evaluating the task's performance of the classifier for each fold, the model's average classification demonstration is computed. The CNN convolution layer activation may be used to see what properties CNN has learned after training.