September 10, 2025Middle East Research Journal of Engineering and Technology

Brain Tumor Detection Using Deep Convolutional Neural Network

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Authors

MPM. P.Bharathiar University GBG Prakash Babu VLVasamsetti LikhithaREVA University

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Implication

This analysis demonstrates improved classification accuracy of brain tumors in MRI scans using deep learning techniques, highlighting the potential of AI in radiology.

Key Points

The model achieved 98.70% validation accuracy, displaying effective classification of brain tumors.
Using the ResNet-18 architecture, the model differentiates various tumors, including meningiomas and gliomas.
Data augmentation methods enhanced model resilience, ensuring robustness against overfitting.
These results suggest the approach could assist in faster and more accurate diagnosis by medical professionals.

Cite This Study

P. et al. (2024) studied this question.

synapsesocial.com/papers/68c2443bb210217d647aaa49 https://doi.org/10.36348/merjet.2024.v04i04.011

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Implication

This analysis demonstrates improved classification accuracy of brain tumors in MRI scans using deep learning techniques, highlighting the potential of AI in radiology.

Brain Tumor Detection Using Deep Convolutional Neural Network

Discussion

Member takes

Implication

Key Points

Cite This Study

Brain Tumor Detection Using Deep Convolutional Neural Network

Discussion

Member takes

Implication

Key Points

Cite This Study

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