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Recent Computational Methods for Pathological Brain Detection
Version 1
: Received: 16 December 2023 / Approved: 25 December 2023 / Online: 26 December 2023 (09:36:55 CET)
How to cite: Chen, X. Recent Computational Methods for Pathological Brain Detection. Preprints 2023, 2023121946. https://doi.org/10.20944/preprints202312.1946.v1 Chen, X. Recent Computational Methods for Pathological Brain Detection. Preprints 2023, 2023121946. https://doi.org/10.20944/preprints202312.1946.v1
Abstract
Pathological Brain Detection (PBD) is a crucial field aimed at identifying and diagnosing structural or functional abnormalities in the brain related to neurological, neurodegenerative, or psychiatric disorders. This detection process typically employs medical imaging technologies such as MRI, CT, or PET scans, along with neurological evaluations, blood tests, and other diagnostic tools. Early detection is essential for effective treatment, improving prognosis, and enhancing the quality of life for affected individuals. Each common computational method provides unique insights into brain pathologies. Despite significant advancements in technology and methodology, PBD faces challenges such as variability in brain anatomy, the complexity of disorders, data quality, standardization issues, interpretability of models, and ethical concerns. Addressing these challenges necessitates collaboration among researchers, clinicians, and policymakers to develop robust and ethical methods for improving the detection and diagnosis of brain disorders.
Keywords
Pathological Brain Detection; Neuroimaging Techniques; Computational Methods
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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