# A Deep Analysis of Brain Tumor Detection from MR Images Using Deep Learning Networks
## 2 Related Works
They achieved an accuracy that was about 5% higher than that of the previous researchers, who performed a nearly identical study. To validate their findings, they also applied a variety of techniques, including fuzzy C-means, SVM, and random forests. To evaluate their results, they considered the metrics of the accuracy, precision, and recall. After evaluating their proposed model, they obtained an accuracy of 98.20%, a recall of 95.13%, and a precision of 93.21%, which were better than the other models. The main weakness in this study was that the researchers only focused on the entire tumor segment, and they did not takeinto account many tumors per slice. [PERSON] et al. [40] introduced a hybrid CNN model to detect brain tumors using BRATS MR images. The analysis and validation were performed on the effectiveness of a unique two-phase training method and sophisticated regularization approaches, such as dropout. Their suggested hybrid model combined two- and three-path networks, which enhanced the model's performance. The model may be effective for a variety of segmentation tasks, according to the capacity analysis of the CNNs, and better performance may be obtained with more training instances. After examining their model, they discovered that their Dice score was 86%, their sensitivity was 86%, and their specificity was 91%. [PERSON] et al. [15] proposed a KNN classifier for the early detection of abnormalities of the fetal brain. They also considered other classifiers such as RF, NB, and RBF for their research. After evaluating their selected models, they obtained an accuracy of 95.6% and an AUC of 99% for their KNN classifier. To obtain better results, they would need to collect a large number of fetal brain images for their ongoing research. [PERSON] et al. [41] proposed a deep-learning-based machine learning architecture for the early diagnosis of embryonic neurodevelopmental abnormalities (ENDs). After evaluating various models, they found that their proposed framework showed promising results for detecting ENDs. [PERSON] et al. [42] used a physiological MRI approach in combination with nine different common machine learning models for the early detection of brain tumors. Many performance indicators, including the accuracy, precision, F-score, AUROC, and classification error, were taken into account when evaluating the model. In their research, they mentioned that ML-based radiophysiomics might be helpful in detecting brain tumors in the clinical setting. [PERSON] et al. [43] proposed an automated method for brain tumor detection using MR images. They discovered after evaluating the proposed ML model that it demonstrated superior classification performance compared to existing approaches, showing a 98.95% accuracy. In our research work, we collected different types of brain tumor MR images. To assess how well our proposed CNN model worked, we also took into account various machine learning models. In our research, the CNN achieved better results compared to the transfer learning models. However, other authors have shown good results when working on transfer learning models and achieved results better than 90% [44]. In our future research, we took this into account and tried to deeply understand this issue. In contrast to the majority of other researchers, we utilized a large dataset of 3264 MRI scans, which is considerably more than what is typically used in many studies. Initially, our system took a long time to process because of the low GPU resources, but we improved the system and lowered the training time. Though other research works had some limitations, we worked to enhance our method, shorten the training period, and increase the performance.