Evaluation of Classification of Brain Tumors Using Convolutional Neural Network Algorithm

Brain cancer is on the rise globally, with a significant increase in adult brain tumor cases in the last two decades. Detecting and treating brain tumors is challenging due to delayed diagnosis, asymptomatic presentation, and size and shape variations. Gliomas, slow-growing brain tumors, are classified by grade and type. These classifications are useful in predicting the tumor's growth rate and likelihood of recurrence. Brain tumors are categorized as benign or malignant. Medical image processing methods can be time-consuming, and accurate grading and typing guidance are scarce. To overcome these challenges, convolutional neural networks (CNN) are a deep learning model that can automatically learn and extract notable features from MRI images and selected machine learning tools to accomplish accurate classification of brain tumors. It is important to recognize brain tumors early on so that treatment can be given early in the progression of the disease. In this study, we propose evaluating of classification of brain tumors using CNN algorithms. The result achieved better performance, where the results of the confusion matrix achieved, an accuracy of 97%, precision of 97.7%, recall of 96.5%, and F1-score of 97.1%. The model has been validated using benchmark datasets from Kaggle which contains 3060 MRI images of brain tumors. The study findings indicate that overall model performance shows potential effectiveness for brain tumor classification. Finally, further readings have been provided.