Detection of Brain Tumors from MRI scans using Deep Learning Model

The diagnosis of brain tumors dominates the medical sector and great accuracy along with efficiency in their methods to detect the tumor is required. The ability to diagnose brain cancers from MRI data has been greatly aided by recent developments in computer-aided diagnosis (CAD) systems. Recent years have seen improvements in approaches used, for instance with CNN or tumor images being automated. In this work was proposed a model that is based on deep learning using Inception-V3 for the classification and localization of brain tumors. Our model was validated on a publicly available dataset and it was shown that the proposed model is accurate and robust in distinguishing among different kinds of brain tumors. The paper also addresses issues such as data imbalance and model interpretability and the feasibility of integrating the system to be used clinically for real time applications.

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Publication Details

Published
2025-03-07
DOI
https://doi.org/10.1109/csnt64827.2025.10968720
Citations
12
Primary Topic
Brain Tumor Detection and Classification
Type
article
Field-Weighted Citation Impact
10.11
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article

Detection of Brain Tumors from MRI scans using Deep Learning Model

Y. Bhargava Sai Surendra, S Hupesh Naga Ketan, Mohebba Naaz
12 citations
Brain Tumor Detection and Classification
10.11
article

Detection of Brain Tumors from MRI scans using Deep Learning Model

Y. Bhargava Sai Surendra, S Hupesh Naga Ketan, Mohebba Naaz
article en
12 citations

Abstract

The diagnosis of brain tumors dominates the medical sector and great accuracy along with efficiency in their methods to detect the tumor is required. The ability to diagnose brain cancers from MRI data has been greatly aided by recent developments in computer-aided diagnosis (CAD) systems. Recent years have seen improvements in approaches used, for instance with CNN or tumor images being automated. In this work was proposed a model that is based on deep learning using Inception-V3 for the classification and localization of brain tumors. Our model was validated on a publicly available dataset and it was shown that the proposed model is accurate and robust in distinguishing among different kinds of brain tumors. The paper also addresses issues such as data imbalance and model interpretability and the feasibility of integrating the system to be used clinically for real time applications.

G Pulla Reddy Dental College & Hospital (IN)
Openalex Percentile: Top 2%
Brain Tumor Detection and Classification
10.11
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