ADVANCING BRAIN PATHOLOGY CLASSIFICATION THROUGH AN IMPROVED HYBRID DEEP LEARNING STRATEGY
Keywords:
.Abstract
The accurate classification of brain pathologies is critical for early diagnosis, treatment planning, and improving patient outcomes. Traditional diagnostic methods,while effective, often suffer from limitations in precision, speed, and consistency.
References
M. Chen et al., “Edge Cognitive Computing Based Smart Healthcare System,” Future Generation Computer Systems, vol. 86, 2018, pp. 403–11.
G. Muhammad et al., “Edge Computing with Cloud for Voice Disorders Assessment and Treatment,” IEEE Commun. Mag., vol. 56, no. 4, Apr. 2018, pp. 60–65.
P. Yang et al., “Lifelogging Data Validation Model for Internet of Things Enabled Personalized Healthcare,” IEEE Trans. Systems, Man, and Cybernetics:
Systems, vol. 48, no. 1, Jan. 2018, pp. 5064.
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Published
2024-08-13
How to Cite
Dr C Mohammed Gulzar, B Ganesh, G Harshavardhan, E Satish Kumar. (2024). ADVANCING BRAIN PATHOLOGY CLASSIFICATION THROUGH AN IMPROVED HYBRID DEEP LEARNING STRATEGY . Journal of Computational Analysis and Applications (JoCAAA), 33(07), 1883–1890. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2214
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