BRAIN TUMOR DETECTION USING MULTIMODEL CONVOLUTIONAL NEURAL NETWORKS

Abstract

A crucial method in medical diagnosis is using deep learning models with neuro assessment to detect and categorize brain cancers. One of the goals of this study is to use deep learning techniques, such as recurrent neural networks (RNNs) and multimodel convolutional neural networks (MCNNs), to automatically find and classify brain cancers in magnetic resonance imaging (MRI) scans. The first step is to gather a wide dataset of labeled MRI images of different tumor kinds. After that, we use preprocessing methods to eliminate noise and ensure the data is uniform. Next, we use the preprocessed dataset to train an adequate deep-learning architecture. Accuracy, precision, recall, and F1-score are some of the conventional measures used to assess the model's performance. Our model achieved a total accuracy of 99.94%, a recall of 99.98%, an F1-score of 99.945%, and a precision of 99.967%, according to the findings. These measures show that our method is successful in identifying and categorizing brain cancers using magnetic resonance imaging (MRI) scans. . Continuous monitoring and modifications enable the model's accuracy and dependability to improve. This research holds the potential to revolutionize the diagnosis of brain tumors, offering crucial insights for swift and precise patient care.