# Brain Tumor Detection with Deep Learning Methods' Classifier Optimization Using Medical Images
## 5 Conclusions  
The most important issue in medicine is the early diagnosis and treatment of diseases. Therefore, early diagnosis is vital, especially in cancer. It is very important for patients to start the treatment process early and manage the entire process correctly. This critical situation constitutes the basic principle of this study. The main purpose of this study is to assist healthcare professionals by utilizing artificial intelligence technologies applied in the field of healthcare. In conclusion, the research presents a comprehensive and innovative approach to the classification and diagnosis of brain tumors using artificial intelligence, specifically employing convolutional neural networks (CNNs) such as VGG, ResNet, DenseNet and SqueezeNet. The study utilized a sizable dataset of 7022 brain MR images obtained from the Kaggle library, which was divided into 40% for testing and 60% for training to ensure unbiased evaluation. Then, training and testing were carried out using this dataset. As a result of classifying four different CNN architectures with machine learning methods, the highest accuracy of 85% was found with the SVM classification method of the DenseNet architecture. In addition, a success rate of 83% was achieved by classifying the hybrid algorithm created with features extracted from four different CNN architectures with LDA. In the second part of the experiments, it is seen that the ResNet architecture reached 99% accuracy in the classification made with default parameter values before parameter optimization. Here, a 100% success rate was achieved as a result of optimizing the Resnet algorithm parameter functions and re-applying them to the test set. When we examine the studies conducted in the literature for this dataset and the ResNet model, we see that such a high success rate has been achieved for the first time. Finally, community learning was \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline Glioma & 1.0 & 0.99 & 1.0 & 0.9987 \\ \hline Meningioma & 0.99 & 1.0 & 1.0 & 0.9987 \\ \hline No-Tumor & 1.0 & 1.0 & 1.0 & 1.0 \\ \hline Pituitary & 1.0 & 1.0 & 1.0 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Voting method performance metric values. Figure 22: Voting method confusion matrix. performed in the classification and a 99% success rate was achieved by applying the voting method. Furthermore, the utilization of ensemble learning methods added another layer of sophistication to the classification process, ultimately contributing to the identification of the most effective validation method. The entire research was conducted using the Python programming language, emphasizing the adaptability and versatility of AI applications in the biomedical field. The findings of this study not only contribute to the growing body of knowledge in the domain of biomedical image processing but also highlight the potential of artificial intelligence, particularly ResNet architecture, in achieving highly accurate classifications in the context of brain tumor diagnosis. The 100% accuracy rate attained by ResNet underscores its robustness and effectiveness in handling complex medical imaging tasks. In comparison with the existing literature, the research results were benchmarked, showcasing competitive or superior performance. The systematic evaluation and comparison of various architectures and machine learning methods contributes to a deeper understanding of their applicability in real-world scenarios. Overall, this research underscores the promising role of artificial intelligence in advancing diagnostic capabilities in the field of medical imaging, offering new possibilities for accurate and efficient brain tumor classification. Author Contributions: Data supply, M.G. ; methodology, M.G. and E.N. ; writing original draft preparation and review, M.G. and E.N. All authors have read and agreed to the published version of the manuscript. This study is derived from [PERSON]'s PhD Thesis.