Automated Brain Tumor Detection and Classification Using Convolutional Neural Networks

Manual inspection is a commonly used method for diagnosing brain tumors. However, it is known to be time-consuming and subjective, which can be a drawback of this approach. This solution aims to address the problem at hand by presenting a research-backed approach that suggests an automated method for diagnosing and classifying brain cancer. The proposed method relies on convolutional neural networks (CNN) to achieve accurate results. In this approach, convolutional neural networks (CNNs) are used for preprocessing, classification, and feature extraction. Improved data quality is achieved through the use of high-resolution brain MRI images from multiple datasets, along with the implementation of state-of-the-art image preprocessing techniques. The CNN architecture has been meticulously designed to improve sensitivity and adaptability by incorporating an attention mechanism and multi-scale feature fusion. The proposed method achieves an impressive accuracy rate of 99.76% in identifying and classifying brain tumors, surpassing the accuracy of traditional human review procedures. Based on the research conducted, it has been discovered that the implementation of automated neuroimaging diagnostics has the potential to greatly enhance the accuracy and efficiency of brain cancer diagnosis. With an accuracy rate of 99.76 % , the proposed method outperforms traditional human review procedures in identifying and classifying brain cancers.