## 5 Discussion  
The main disadvantage of deep learning models is the high computational resources required, such as GPUs and a large RAM size. During the training phase, the CNN classifier generates synthetic data, which results in a high storage capacity requirement. However, as shown in Figure 1, this high hardware resource requirement can be met by using cloud computing services in our proposed IoMT framework. Furthermore, in this study, all hyperparameter values of our CNN classifier and implemented deep network models were manually tuned. This manual tuning procedure is an iterative and time-consuming task that must be completed to achieve good classification results. For a large number of hyperparameters, the grid-based search is computationally expensive.  
Figure 8: Analysis of testing/training split ratio for the CNN model.  
Figure 7: Confusion matrix of best model after hyper-parameter optimization: (**a**) best CNN model, (**b**) best ANN model.  
Deep networks had previously outperformed shallow strategies in a variety of ML situations, however, this is no longer the case. One such explanation might be a lack of parameter search. Another explanation might be the tiny size of the data. Deep networks are extremely sensitive to the size of the training set and require substantially larger training data sets to be properly established. Our results show that no single hyperparameter combination outperforms the others significantly. Because of changes in weight and bias initialization, training a model with the same hyperparameter values once more does not always result in the same classification accuracy. As a result, it is essential to repeat training several times before selecting the best performing network. However, deeper networks with more layers generally take longer to train.  
The main advantage of the current study is the optimization of the hyperparameter values. Hyperparameter tuning is an important aspect of regulating a machine learning model's behavior. Our estimated model parameters yield inferior outcomes if our hyperparameters are not appropriately tuned, since they do not minimize the loss function. Modern machine learning methods typically include many hyperparameters (from one to thousands), which are critical for model's generalizability. This activity requires professional knowledge and expert experience. Moreover, the time requirements to perform search over full hyperparameter spaces are huge. The hyperparameter search usually only trains a limited number of candidate configurations with limited training time, and only the most promising candidates undergo full training. The question of how to build a new hyperparameter optimization strategy that incorporates all of the advantages of both automation and expert knowledge remains unsolved.  
As a result, in our future studies, we will use more sophisticated neural architecture search methods [59] to automate the design of our developed CNN classifier. The security and privacy of patient data and breast cancer diagnosis results will also be considered in our proposed medical IoT-based system, which will be used for open communication and networked computing systems. However, the proposed IoMT system, which includes our developed CNN classifier, is still valid for achieving successful automated diagnosis of breast cancer.