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Sains Malaysiana 55(3)(2026): 589-900
http://doi.org/10.17576/jsm-2026-5503-18
An
Innovative Algorithm-Assisted Neuroimaging Technique for Calculating Brain Age
(Teknik Inovatif
Pengimejan Neuro Berbantukan Algoritma untuk Mengira Usia Otak)
VIJAYABALAN, D.
Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering
College, Chennai, TamilNadu, India 600062
Received: 13 January 2025/Accepted: 20
February 2026
Abstract
Brain scans and
machine learning algorithms can now be used to determine a person's age. In
this assessment, we discuss a brief summary of the multiple medicinal purposes
of brain-age estimation in neuropsychiatry and general populations. This
verified technique has created new opportunities for resolving clinical
concerns in neurology. For the purpose of developing a framework for brain-age
projection, we first give an overview of common neuroimaging modalities,
feature extraction techniques, and machine learning models. In this study,
we proposed a novel wild horse optimized multi-tiered convolutional neural
network (WHO-MCNN) strategy for predicting brain age. We employed magnetic
resonance imaging (MRI) to collect brain neuroimage data for this study. To
retain edges and reduce noise in images, pre-processed data was exposed to a
bilateral filter. The histogram of oriented gradients (HOG) was used to extract
the features from the data to record shape and texture information that is
valuable for object recognition. The proposed method is further compared to
other machine learning algorithms. The results show the proposed method
achieved better performance in MAE, RMSE, and R2, such as 2.982,
3.925, and 0.537 for brain age prediction. Through early identification and
treatment of age-related neurological diseases, this approach facilitates a greater
understanding of brain aging processes. Finally, we offer some recommendations
for future study approaches and talk about the real-world issues and
difficulties that have been discussed in the literature.
Keywords: Brain age; magnetic resonance imaging (MRI); neuroimage;
wild horse optimized multi-tiered convolutional neural network (WHO-MCNN)
Abstrak
Imbasan otak dan algoritma pembelajaran mesin kini boleh digunakan
untuk menentukan umur seseorang. Dalam penilaian ini, kami membincangkan ulasan
ringkas tentang pelbagai tujuan perubatan anggaran usia otak dalam
neuropsikiatri dan populasi umum. Teknik yang disahkan ini telah mewujudkan
peluang baharu untuk menyelesaikan kebimbangan klinikal dalam neurologi. Bagi
tujuan membangunkan rangka kerja untuk unjuran usia otak, pertama sekali kami
memberikan gambaran keseluruhan modaliti pengimejan neuro biasa, teknik
pengekstrakan ciri dan model pembelajaran mesin. Dalam kajian ini, kami
mencadangkan strategi rangkaian saraf konvolusi berbilang peringkat (WHO-MCNN)
yang dioptimumkan oleh kuda liar baharu untuk meramalkan usia otak. Kami
menggunakan pengimejan resonans magnetik (MRI) untuk mengumpul data imej neuro
otak untuk kajian ini. Untuk mengekalkan pinggiran dan mengurangkan hingar
dalam imej, data pra-proses didedahkan kepada penapis dua hala. Histogram
berorientasikan kecerunan (HOG) digunakan untuk mengekstrak ciri daripada data
untuk merekodkan maklumat bentuk dan tekstur yang penting untuk pengecaman
objek. Kaedah yang dicadangkan ini dibandingkan dengan algoritma pembelajaran
mesin yang lain. Keputusan menunjukkan kaedah yang dicadangkan mencapai
prestasi yang lebih baik dalam MAE, RMSE dan R2, seperti 2.982, 3.925 dan 0.537
untuk ramalan usia otak. Melalui pengenalpastian dan rawatan awal penyakit
neurologi berkaitan usia, pendekatan ini memudahkan pemahaman yang lebih
mendalam tentang proses penuaan otak. Akhir sekali, kami menawarkan beberapa
cadangan untuk pendekatan kajian masa depan dan membincangkan isu dan kesukaran
dunia sebenar yang telah dibincangkan dalam kepustakaan.
Kata kunci: Imej neuro; pengimejan resonans magnetik (MRI); rangkaian
neural konvolusi berperingkat yang dioptimumkan oleh kuda liar (WHO-MCNN); usia
otak
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*Corresponding
author; email: vijayabalantqb@gmail.com
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