A Novel Approach to Brain Tumor Classification Using RNN and CNN Algorithm

They are aiming to establish a solution for brain tumors that are prevailing neurological diseases and subsequently leading to cancer-related deaths. Correct classification of brain tumors is important for proper diagnosis and planning treatment. Existing methods for brain tumor classification are performed manually and tend to be subjective, not to mention time-consuming. Hence, we require an automated and effective mechanism for brain tumor classification. The present study introduces a novel ensemble procedure utilizing a combination of the Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) for brain cancer categories. The LSTM RNN is used to analyze the time-varying features of the magnetic resonance imaging (MRI) scans, whereas 3D CNNs are exploited for spatial appearance descriptions. These two network types collaborate in the brain tumor classification process to determine each class according to its properties. This method enhances the accuracy and swiftness of brain tumor classification. They trained their RNN-CNN model using thousands of MRI scans and found that they could be used to distinguish between these different types of bratypesumors accurately. In addition, our method also decreased processing time compared with standard methods used for manual analysis. Finally, the overall results suggest that our proposed method could be an efficient automated solution for brain tumor classification with the potential to improve early diagnosis and treatment planning of patients suffering from such malignant disease. It is also readily integrated into current health systems, providing it a worthwhile position for neurologists.