## 5 Result and Discussion
It is fascinating to compare the outputs of the proposed CNN model Deep-Hist to those of current dominant advanced CNN models such as ResNet, DenseNet, and MobileNetV2. The same experiment is run with the same combined dataset (COVID-19, BreakHis, NIH-Xray, and BraTS), using well-known pretrained CNNs such as ResNet, DenseNet, and MobileNetV2. Table 4 shows the outcomes of these models. The proposed CNN model Deep-Hist and many common architectures are compared in terms of accuracy and AUC obtained during the experiments. Tables 4 and 5 shows that the proposed CNN model Deep-Hist outperforms other networks in the classification test. In the task of classification, the DenseNet model, which is close to the proposed CNN model, achieves an accuracy of 87.59%. Pretrained deep learning frameworks for common image classification issues are constructed and learned on generic data sets, which might explain why the proposed CNN models outperform them. On the other hand, the proposed CNN model is designed for a more specific task, namely Breast Cancer classification. In addition, the proposed model Deep-Hist is trained and evaluated using Histopathological images of Breast Cancer, NIH-Xray images of lungs, COVID-19 images of lungs X-ray, and Brain tumors of BraTS. Another factor why the proposed CNN model \"Deep-Hist\" surpasses the pretrained models is that the proposed CNN architecture was enhanced specifically for the categorization job and used the hyperparameters that generate the optimal results. Figure 3: The iteration of the combined dataset to acquire optimal results using various hyperparameters such as learning rate (depicted in the first row), batch size (presented in the middle row), and activation function (shown in the last row) and its values. Figure 4 also shows that a recall of 0.5 percent denotes that the classification algorithm has a large number of false negatives, which can be caused by imbalanced classes or untuned model hyperparameters, whereas a recall of 1.0 percent demonstrates that the classifier has confidently predicted for the extracted features. Furthermore, when the Area Under the Curve (AUC) is 1 or close to 1, the classification algorithm has successfully discriminated all positive and negative class points, but when AUC is zero, the classification algorithm classified all negatives as positives and vice versa. The True Positive Rate (TPR) is shown against the False Positive Rate (FPR) at varying levels of intensity on the Receiver Operating Characteristics (ROC) Curve. Figure 4 illustrates how classifiers with curves closer to the upper left corner perform better. In addition, when the classifier curve approaches the ROC space's 45\({}^{\circ}\) diagonal, the test becomes less accurate. A probability in the range [0.0, 0.49] implies a negative result (0), whereas a probability in the range [0.5, 1.0] suggests a positive event (1). The Deep-Hist accuracy is 0.95, ResNet = 0.87, MobileNetV2 = 0.84, and DenseNet = 0.88 are closer to the top left corner in the experimental findings. The two initial hyperparameters which as epochs and kernel/filter values steer the search toward the local maximum on the right side; however, exploration forecasts the algorithm to baffle from that topical optimum and determine the global on the left. We observe how the two hyperparameter point ideas frequently occur in high-uncertainty zones (exploration) and are not just based on the high-degree surrogate function values.