A novel CNN-MLP hybrid deep learning model is introduced for precise brain age prediction

In the realm of brain age prediction, a revolutionary hybrid deep learning model has emerged, fusing the powers of Convolutional Neural Networks (CNN) and Multilayer Perceptron (MLP) architectures. The core challenge lies in the precise estimation of an individual’s brain age — an indispensable metric for unraveling the intricacies of both normal and pathological aging processes. Traditional models often disregard the impact of sex-related factors on brain age prediction, ushering in the era of innovation. While conventional brain age prediction models primarily hinge on structural brain Magnetic Resonance Imaging (MRI) data, they overlook the invaluable insights embedded in sex-related variables. Enter the cutting-edge CNN-MLP hybrid algorithm, which sets itself apart by incorporating brain structural images and factoring in sex information during the model construction phase. This novel approach distinguishes itself by proactively addressing sex-related effects, a stark departure from post-validation adjustments, underscoring its potential for elevated precision and clinical relevance. <a href="https://medium.com/@multiplatform.ai/a-novel-cnn-mlp-hybrid-deep-learning-model-is-introduced-for-precise-brain-age-prediction-36c689594d71">Learn More</a>