Convolutional neural networks in brain disease diagnosis: a unified review of Alzheimer's, Parkinson's, and brain tumor classification.

Overview

1. School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
2. Computer Science and Engineering, Indian Institute of Information Technology, Kottayam, Kerala, India

Abstract

Neurological and neuro-oncological brain disorders like Alzheimer's disease (AD), Parkinson's disease (PD), and brain tumors are challenging to diagnose due to overlapping symptoms and the limitations of conventional imaging techniques. Magnetic resonance imaging (MRI) with convolutional neural networks (CNNs) has emerged as a powerful approach, enabling automated and high-precision detection and staging. This review critically integrates recent developments in CNN architectures, such as hybrid models, attention mechanisms, and 3D CNNs for MRI-based diagnosis of these disorders. It further examines preprocessing methods, datasets, and performance metrics across studies, with emphasis on innovations such as transformer-based models and lightweight architectures. While CNNs show impressive accuracy, issues remain in generalizability, interpretability, and clinical integration. This review highlights the need for multimodal data fusion, explainable artificial intelligence, and real-world validation to narrow the gap between research and clinical practice. By defining future directions, this review aims to guide the development of robust, scalable neurodiagnostic systems for early intervention and better patient outcomes.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• kaggle.com/datasets/ashkhagan, at Kaggle; found in the resources table
• kaggle.com/datasets/katalniraj, at Kaggle; found in the resources table
• kaggle.com/datasets/masoudnickparvar, at Kaggle; found in the resources table
• kaggle.com/datasets/shreyag1103, at Kaggle; found in the resources table
• kaggle.com/datasets/tourist55, at Kaggle; found in the resources table
• kaggle.com/datasets/uraninjo, at Kaggle; found in the resources table
• openneuro:ds000245, at OpenNeuro; found in the resources table

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 3 authors, 6 keywords, 97 references.

Cite

This paper

Nathea, R., Ghosh, K., & Nisha, J. S. (2026). Convolutional neural networks in brain disease diagnosis: a unified review of Alzheimer's, Parkinson's, and brain tumor classification. Frontiers in neuroscience, 20, 1875642. https://doi.org/10.3389/fnins.2026.1875642

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