# Brain Tumor Detection with Deep Learning Methods' Classifier Optimization Using Medical Images
## 1 Introduction  
Artificial intelligence is a technology consisting of extremely intelligent programs and machines that try to resemble human intelligence and offer creative solutions to humanity with the information they collect. The main purpose of artificial intelligence is to adapt the intelligent behavior observed in humans to computers and machines. In recent years, artificial intelligence technologies have been used frequently in the defense industry, economy, social life and health sector.  
Machine learning, which is a subset of artificial intelligence, is the system that creates the architectures of intelligent algorithms that can make predictions through self-learning models. At the same time, there are many studies in the literature on deep learning and image processing, which is an area of machine learning. These studies have focused especially on image extraction, classification and object detection. The success rates of different algorithms on different and similar datasets have varied in studies [1].  
The interpretation of radiologic images always requires precision and care. In diseases where X-ray and computed tomography images are used frequently, such as the coronavirus (COVID-19) epidemic in recent years, the responsibilities of specialists have increased; however, the examination of radiological images has become more time-consuming. Specialized physicians working in the field of radiology work very carefully and meticulously. As a result, a report is created by interpreting MR images of many different parts of the body (brain, chest, leg, etc.) and according to these results, specialist physicians in different branches make comments for treatment and diagnosis [2].  
Image processing is a very important field of study and its applications in the medical field make it even more important. A mistake made here can have critical consequences for patients. For this reason, experts working in this field have to be very careful [3]. Brain tumors are known to be highly sensitive due to their location. Interpreting MRI images from hundreds of thousands of different patients can sometimes be complex. It is sometimes difficult for specialists to understand whether tumors of different sizes and shapes are benign or malignant. Computer-based systems are needed to facilitate this situation and minimize the margin of error. For this purpose, deep learning methods are of interest to detect tumor cells more accurately and quickly [4].  
The vast majority of biomedical image classification studies in the literature have relied on a single CNN model for feature extraction. However, this approach has limitations in terms of feature extraction and performance. Methods that focus on a single model may fail to capture complex brain MRI images and variability. As a result, classification accuracy remains low. In contrast, our study proposes a hybrid method for brain tumor diagnosis. The proposed method uses different CNNs trained to extract features from brain MRI images and multiple ML classifiers to classify brain images into four categories. Here, the brain tumor classes consist of normal and three tumor images. The features extracted from five different ML classifiers and four different trained models are evaluated to select and evaluate the strongest features, and the features extracted from various trained CNN models are combined to develop a feature ensemble approach to tackle the brain image classification problem. The hybrid model extracted from ensemble learning is further classified and tested for accuracy. This approach combines different information gathered by multiple CNN models rather than relying on features extracted from a single model. In addition, in order to improve the model's accuracy, the proposed approach is optimized by optimizing the grid search. The dataset was classified with the most effective ML classifier after optimization. The experimental results show that our proposed hybrid method significantly improves the performance.