Hybrid model for brain age prediction on MRI images with modified texture features

Abstract

Machine learning (ML) and diffusion magnetic resonance imaging (dMRI) approaches can be employed to identify individual brain aging conditions in both healthy people and patients with brain diseases. This research establishes a new brain age prediction system, which comprises different phases like pre-processing, feature extraction, feature selection, and prediction. The original magnetic resonance imaging (MRI) picture is first carried out in a pre-processing stage, which enhances image quality as well as visual appearances. Following that, the shape, intensity, as well as texture-based characteristics are retrieved. Furthermore, texture features like modified entropy, as well as modified correlation are retrieved along with the traditional features. Then, a modified principle component analysis (PCA) method is utilized for feature selection stage. In the prediction phase, hybrid classifiers such as multi-layer perception (MLP) as well as long short term memory (LSTM) are used. The weight of LSTM and MLP is optimally tuned with the aid of an implemented self-updated red deer algorithm (SURDA) model to make an accurate prediction. Moreover, the suggested work outperforms other traditional works in terms of MSE, MAPE, MSLE, and MAE, correspondingly.