## 5 Result and Discussion
HyperDistance(\(\lambda\), \(\lambda^{\prime}\)) else if\(\lambda^{\prime}\) is \(\in\mathbb{R}^{\prime}\)then  Create a surrogate model (Gaussian Process model) Ensure: Using a hybrid acquisition function (AF), we retrieve the optimal hyperparameter value F\({}^{\prime}\), HyperDistance(\(\lambda\), \(\lambda^{\prime}\)) \(F^{\prime}\leftarrow\varkappa^{*}orf\)  Update the statistical Acquisition Function (AF) model, \(\Omega^{\prime}\)+= \(\lambda\)  Train the different pretrained model and the proposed model Deep-hist using optimized hyperparameters (\(\Omega^{\prime}\)). Compare the accuracy and F1-Score and re-evaluate with the next closest hyperparameter. We normalize the accuracy and F1-Score of the training of different training iteration by setting \(\big{[}\frac{\alpha_{i}^{2}-\alpha_{i}min_{i}^{\eta}}{\alpha_{i}\_max_{i}^{ \eta}-\alpha_{i}min_{i}^{\eta}},\ldots,\frac{\alpha_{i}^{\eta}-\alpha_{i}min_{ i}^{\eta}}{\alpha_{i}\_max_{i}^{\eta}-\alpha_{i}min_{i}^{\eta}}\big{]}\). endif endwhile procedureHyperDistance(\(\lambda\), \(\lambda^{\prime}\))  For each vector of hyperparameter \(\lambda_{i}\in[\lambda_{0}\), \(\lambda_{1}\), \(\lambda_{2}\)...\(\lambda_{N}]\) determine the accuracy w.r.t closest next expected hyperparameter \(\lambda^{\prime\prime}\) using Euclidean Distance. return\(\lambda\) endprocedure
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**Algorithm 1** Proposed algorithm Adaptive Hyperparameter Tuning (AHT). The quality of the recommended model is evaluated using the 5-fold cross-validation technique for binary classification. The data set is divided into five sections, four of which are used for training and the fifth for testing. The trials are repeated five times. After evaluating the task's performance of the classifier for each fold, the CNN model's average classification achievement is computed. Because the study contains 23,950 images, there is enough dataset to divide them into 80:10:10 training, validation, and test sets. For testing the trained model, images are chosen at random from each class's combined dataset to test the model. After 60 iterations, the proposed CNN model for the classification task achieves 95.71 accuracies. These findings back up the CNN model's ability to classify various types of malignant images. See Tables 4 and 5 and Figures 3 and 4 for further information on accuracy measurements such as precision, recall, specificity, accuracy, and sensitivity. As shown in Figure 4, the AUC of the ROC curve is 0.9534 and in Table 4, ResNet had an 87.76 accuracy rate, DenseNet had an 87.59 accuracy rate and MobileNetV2 had an 84.18 accuracy rate. Large and readily available healthcare data sets are used to obtain acceptable categorization outputs. The classification of malignant types is accurate at 95.71%. The plausible outcomes of the proposed framework are evaluated using performance assessment metrics such as ROC curve AUC, precision, specificity, accuracy, and sensitivity. The use of pretrained Convolutional Neural Network models and our proposed model Deep-Hist to categorize images has lately become popular in the diagnosis of medical analysis. The kind of malignant disease is determined using CNN models in this study. The key hurdle with CNN is figuring out which network architecture is the most successful for a specific situation. Choosing the right hyperparameters is crucial for getting good results, especially with convolutional neural networks. Adaptive Hyperparameter Tuning (AHT) is proposed in this study to construct the most effective CNN framework and to improve the hyperparameters of the CNN models. Large and readily available healthcare data sets such as BreakHis, BraTS, and NIH-Xray are used to obtain plausible classification outputs. The classification of malignant types is accurate at 95.71%. The outcomes of the proposed framework are evaluated using performance assessment metrics such as ROC curve AUC, precision, specificity, accuracy, and sensitivity depicted in Tables 4 and 5.