# Brain Tumor Detection with Deep Learning Methods' Classifier Optimization Using Medical Images
## 3 Material and Method
### Parameter Optimization  
In deep learning algorithms, hyperparameters are special parameters that control the learning process of the model and need to be tuned. These hyperparameters are the values that drive the architecture of the network and the training process and can influence the success of a particular deep learning model. Correctly tuning the hyperparameters can help the model achieve better performance [38].  
Parameter optimization has become increasingly necessary in the development of deep learning models in recent years, as a result of increasing the number of neural networks to find the best accuracy result and a model designed with fewer weights and parameters. Since the choice of hyperparameters is difficult, it is also difficult to adapt it to experimental values. The tuning of the hyperparameters is a complex and carefully designed structure. For widely used models, hyperparameters can be set manually, as researchers can take examples from previous studies. For small-scale models, hyperparameters can be adjusted manually. But for larger models or newly published models, finding hyperparameters requires a lot of experimentation by researchers [39].  
Hyperparameters can be divided into two groups: those used for model training and those used for model design. Choosing appropriate hyperparameters for model training enables neural networks to learn faster and achieve improved performance. The most widely adopted optimization algorithms for training a deep neural network are momentum and stochastic gradient descent, as well as AdaGrad, RMSprop and Adam. Particle size and learning rate are the most important factors, as these determine the convergence rate of the neural network during the training process. Hyperparameters used for model design are more related to the structure of the neural networks. The most typical example of this is the number of hidden layers and the width of the layers [40]. To explain the most important parameter values:  
Learning Rate: This hyperparameter controls the amount by which the weights of the network are updated. A high learning rate can update the weights quickly, but there is a risk of overdoing it. A low learning rate can slow down the learning process of the model. In most cases, the learning rate must be manually adjusted during model training, and this adjustment is often necessary to achieve high accuracy [41].  
Epoch Number: Epoch means that the entire training data is presented to the model once. The number of epochs determines how many times the model sees all the training data. Too many epochs can lead to overfitting, while insufficient epochs may not allow the model to complete its learning.  
Mini-Batch Size: Batch size refers to the number of samples used in each training iteration. Small batch sizes can generally result in faster training processes, but can affect the overall model performance. Also, the randomly generated training sets used by the Probabilistic Gradient Projection Algorithm are called Mini-Batches. The gradient calculation is performed on the samples in the Mini-Batch set.  
The process of hyperparameter tuning usually involves trial and error. By trying different hyperparameter values, one tries to find the combination that provides the best performance. This process is important to increase the generalizability of the model and avoid overfitting to the training data.