Precision Brain Tumor Detection Using Integrated Batch Normalization

The proposed research introduces a new approach for brain tumor detection, leveraging a sophisticated computational model. A neural network is developed to accurately categorize brain tumor images into four distinct types. A significant innovation within the methodology is the integration of batch normalization after each layer of the neural network. This technique enhances the model’s learning process, facilitating effective data processing and minimizing errors. Thus, the proposed research presents a robust and accurate solution for brain tumor detection, with potential implications for enhancing patient care and treatment outcomes. The system achieves an accuracy rate of 97.47%, indicating its capability to correctly identify tumor types in the majority of cases. Further, the proposed model exhibits a low loss rate of 0.1193, showcasing its precision in making these classifications.