Hybrid deep model for brain age prediction in MRI with improved chi-square based selected features.

Ageing and its related health conditions bring many challenges not only to individuals but also to society. Various MRI techniques are defined for the early detection of age-related diseases. Researchers continue the prediction with the involvement of different strategies. In that manner, this research intends to propose a new brain age prediction model under the processing of certain steps like preprocessing, feature extraction, feature selection, and prediction. The initial step is preprocessing, where improved median filtering is proposed to reduce the noise in the image. After this, feature extraction takes place, where shape-based features, statistical features, and texture features are extracted. Particularly, Improved LGTrP features are extracted. However, the curse of dimensionality becomes a serious issue in this aspect that shrinks the efficiency of the prediction level. According to the "curse of dimensionality," the number of samples required to estimate any function accurately increases exponentially as the number of input variables increases. Hence, a feature selection model with improvement has been introduced in this paper termed an improved Chi-square. Finally, for prediction purposes, a Hybrid classifier is introduced by combining the models like Bi-GRU and DBN, respectively. In order to enhance the effectiveness of the hybrid method, Upgraded Blue Monkey Optimization with Improvised Evaluation (UBMOIE) is introduced as the training system by tuning the optimal weights in both classifiers. Finally, the performance of the suggested UBMIOE-based brain age prediction method was assessed over the other schemes to various metrics. [ABSTRACT FROM AUTHOR]