Optimizing Deep Learning Models for Accurate Multiclass Brain Tumor Classification in Mri Images
Year: 2025
Type: article
Abstract: A brain tumour is an aberrant cell mass that has the potential to become cancerous. Magnetic resonance imaging (MRI) scans are frequently used to identify brain tumours. An MRI can provide information about the abnormal growth of brain tissue. Deep learning is used to construct models for the identification and categorization of brain tumours using MRI. This makes it easier and faster to diagnose brain tumours. Brain tumours can be found more quickly and precisely by using these algorithms to analyse magnetic resonance imaging (MRI) scans, which will help patients receive better care. If brain tumours were consistently detected early and treated appropriately, the death rate might be reduced. This paper also discusses the proposed design and compares it with other models, such as DenseNet-169 and Inception-ResNet-v2. By applying the recommended models, we achieved accuracy levels of 97.52% and 96.89%. In contrast to other models, it yields the best results.
Author: Journal of Theoretical and Applied Information Technology
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7083199826
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