Abstract:
Purpose: It's a crucial and difficult task to define a brain tumor in the process of medical imaging. If the brain tumor can be classified, it might be quite important in improving the patient's likelihood of survival while undergoing therapy, and This project's main objective is to create a model for detecting brain tumours.that can be seen on 2D MRI imaging.
Research gap: If there are no MRI sequences, MRI images of the brain produced by convolutional neural networks may be incorporated into deep learning models.
Problem statement: In the paper, only a 2D image and CNN are used.
Objective: The major objective is to determine whether there is a brain tumor or not, the brain is in good condition. For the purpose of enhancing performance and streamlining the classification process of medical images, the proposed system has been investigated based on CNN and deep learning classifiers.
Methodology: Here, preprocess the data set,split it into two sections,train the model,and finally evaluate the accuracy.
Result: Comparative data demonstrates that the proposed model’s accuracy was higher than that of the previously used method in detecting and classifying tumors.
Conclusion: In this study, it was shown that it is possible to plan treatment for patients with brain tumors by analyzing MRI images produced using a deep learning method based on CNN.
Keywords- CNN, brain tumor, deep learning, classification, artificial intelligence.
