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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.<\/jats:p>","DOI":"10.3389\/fnins.2026.1875642","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T06:03:16Z","timestamp":1785304996000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Convolutional neural networks in brain disease diagnosis: a unified review of Alzheimer's, Parkinson's, and brain tumor classification"],"prefix":"10.3389","volume":"20","author":[{"given":"R.","family":"Nathea","sequence":"first","affiliation":[{"name":"School of Electronics Engineering, Vellore Institute of Technology","place":["Vellore, Tamil Nadu, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kalyanbrata","family":"Ghosh","sequence":"additional","affiliation":[{"name":"School of Electronics Engineering, Vellore Institute of Technology","place":["Vellore, Tamil Nadu, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. 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