Improved Demntia Images Detection And Classification Using Transfer Learning Base Convulation Mapping With Attention Layer And XGBOOST Classifier

Abstract

A classification scheme for the etiology of brain disease based on magnetic resonance imaging is proposed in this paper. Then, attention-based, transfer learning (Extracting variable characteristics of patterns from MRI scans) was used to generate more accurate predictive patterns, and finally, features were trained and used to classify fMRI data to Boost derived hyper-parameters were obtained and evaluated to identify different patterns of dementia risk. Typically-to-to-independent variable extractions are performed by using gradient boosting and then produces derived variables. The MRI in the system is pulled from the ADNI database. When using Feature extractor's technique, we find that most features are extracted at an acceptable speeds. The experimental results proved that the proposed approach can be applied to the task of classifying output in the proper manner. The method would help to increase precision and accuracy by (almost) 4.2 percent, while keeping recall at (virtually) 94.6%