Article: Improving brain MRI segmentation of multiple sclerosis using an advanced CNN approach Journal: International Journal of Business Intelligence and Data Mining (IJBIDM) 2026 Vol.28 No.1 pp.1 - 16 Abstract: Multiple sclerosis (MS) can be detected early by looking for lesions in brain magnetic resonance imaging (MRI). Recently, unsupervised anomaly detection algorithms based on autoencoders were presented for the automatic identification of MS lesions. However, because these autoencoder-based approaches were created exclusively for 2D MRI pictures (e.g., 2D cross-sectional slices), they do not make use of the complete 3D information of MRI. In this research work, a novel 3D autoencoder-based methodological solution for detecting MS lesion volume in MRI is offered. We begin by defining a 3D convolutional neural network (CNN) for complete MRI volumes and then construct each encoder and decoder layer of the 3D autoencoder using 3D CNN. For optimal data reconstruction, we additionally include a skip link between the encoder and decoder layers. In the experimental results, we compare the 3D autoencoder-based method to the 2D autoencoder models using training datasets from the Human Connectome Project (HCP) and testing datasets from the Longitudinal MS Lesion Segmentation Challenge, and show that the proposed method outperforms the 2D autoencoder models by up to 20% in MS lesion prediction. Inderscience Publishers - linking academia, business and industry through research

Title: Improving brain MRI segmentation of multiple sclerosis using an advanced CNN approach

Authors: V. Biksham; Sampath Korra; B. Pradeep Kumar; Salar Mohammad

Addresses: Department of CSE (Data Science), Anurag University, Ghatkesar, Hyderabad – 500088, Telanana, India ' Department of Computer Science and Engineering, CMR Institute of Technology, Kandlakoya, Medchal Hyderabad – 501401, India ' Department of Computer Science and Engineering, Sri Indu College of Engineering and Technology, Sheriguda – 501510, Ibrahimpatnam, India ' Department of CSE (Data Science), Anurag University, Ghatkesar, Hyderabad – 500088, Telanana, India

Abstract: Multiple sclerosis (MS) can be detected early by looking for lesions in brain magnetic resonance imaging (MRI). Recently, unsupervised anomaly detection algorithms based on autoencoders were presented for the automatic identification of MS lesions. However, because these autoencoder-based approaches were created exclusively for 2D MRI pictures (e.g., 2D cross-sectional slices), they do not make use of the complete 3D information of MRI. In this research work, a novel 3D autoencoder-based methodological solution for detecting MS lesion volume in MRI is offered. We begin by defining a 3D convolutional neural network (CNN) for complete MRI volumes and then construct each encoder and decoder layer of the 3D autoencoder using 3D CNN. For optimal data reconstruction, we additionally include a skip link between the encoder and decoder layers. In the experimental results, we compare the 3D autoencoder-based method to the 2D autoencoder models using training datasets from the Human Connectome Project (HCP) and testing datasets from the Longitudinal MS Lesion Segmentation Challenge, and show that the proposed method outperforms the 2D autoencoder models by up to 20% in MS lesion prediction.

Keywords: multiple sclerosis; brain MRI; image segmentation; CNN; chronic disease; healthcare.

DOI: 10.1504/IJBIDM.2026.151261

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.1, pp.1 - 16

Received: 17 Nov 2023
Accepted: 04 Dec 2024
Published online: 20 Jan 2026 *

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Title: Improving brain MRI segmentation of multiple sclerosis using an advanced CNN approach

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