Enhancing Brain Tumor Diagnosis Through Advanced Artificial Intelligence and Improved SITD Techniques

Braintumors are among the most difficult to diagnose because of their nature and high fatality rates. This paperseeks to put forward an enhanced Artificial Intelligence-based model with better Supportive Intelligence for Tumor Detection (SITD) to facilitate brain tumor diagnosis. The proposed model focuses on the deep learning type of approaches for the classification of tumors and the estimation of the severity of the tumor through MRI images from BRATS and TCGA-GBM datasets. As feature extraction, it used convolutional neural networks (CNNs), and an adaptive learning strategy was incorporated for model updating. The system evaluated had an average accuracy of 96.8%, a recollection mean of 95.2%, a specificity mean of 97.5%, and a resistance percent over 98.1%. Pre-processing takes from 750ms to 1.8 sec per image, meaning that it can perform real-time clinical tasks. These results show that the proposed model performs better than the previous approaches. Therefore, the proposed SITD model is a powerful approach to brain tumor detection that improves patient results.