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Visual Intelligence in Neuro-Oncology: Effective Brain Tumor Detection through Optimized Convolutional Neural Networks
Research Article | Published: 20 March 2025
IECE Transactions on Sensing, Communication, and Control Volume 2, Issue 1, 2025: 25-35
Volume 2, Issue 1, IECE Transactions on Sensing, Communication, and Control
Volume 2, Issue 1, 2025
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Xue-Bo JIN
Xue-Bo JIN
Beijing Technology and Business University, China
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IECE Transactions on Sensing, Communication, and Control, Volume 2, Issue 1, 2025: 25-35

Free to Read | Research Article | 20 March 2025
Visual Intelligence in Neuro-Oncology: Effective Brain Tumor Detection through Optimized Convolutional Neural Networks
by
Om Prakash Suthar Om Prakash Suthar 1 *
Yash Zinzuvadia Yash Zinzuvadia 1
Waqas Ullah Waqas Ullah 2
Haseeb Khan Haseeb Khan 3
Chirag Agarwal Chirag Agarwal 4
1 Department of Computer Engineering, Marwadi University, Gujarat 360001, India
2 Mardan Medical Complex, Mardan 23200, Pakistan
3 Northwest School of Medicine, Peshawar 25000, Pakistan
4 Tata Consultancy Services Ltd., Edison, NJ 08837, United States
* Corresponding Author: Om Prakash Suthar, [email protected]
DOI: 10.62762/TSCC.2024.964451
Received: 27 December 2024, Accepted: 14 February 2025, Published: 20 March 2025  
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Abstract
Brain tumor detection (BTD) is a crucial task, as early detection can save lives. Medical professionals require visual intelligence assistance to efficiently and accurately identify brain tumors. Conventional methods often result in misrecognition, highlighting a critical research gap. To address this, a novel BTD system is proposed to predict the presence of a tumor in a given MRI image. The system leverages a convolutional neural network (CNN) architecture, combined with a multi-layer perceptron (MLP) for feature extraction and understanding complex pixel patterns. An extensive ablation study was conducted to empirically analyze and identify the optimal model for the task. The findings demonstrate that the proposed method outperforms existing approaches. Notably, the model with two convolutional layers achieved an accuracy of 85%, while a single-layer model attained an impressive accuracy of 99.6%.
Graphical Abstract
Visual Intelligence in Neuro-Oncology: Effective Brain Tumor Detection through Optimized Convolutional Neural Networks
Keywords
deep learning
convolutional neural network
brain tumor classification
multilayer perceptron
detection system
AI
Data Availability Statement
Data will be made available on request.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest. While Waqas Ullah is an employee of Mardan Medical Complex, Mardan 23200, Pakistan; Chirag Agarwal is an employee of Tata Consultancy Services Ltd., Edison, NJ 08837, United States; and other authors are affiliated with their respective institutions, these affiliations had no influence on the study design, data collection, analysis, interpretation, or publication of the results.
Ethical Approval and Consent to Participate
Not applicable.
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Cite This Article
APA Style
Suthar, O. P., Zinzuvadia, Y., Ullah, W., Khan, H., & Agarwal, C. (2025). Visual Intelligence in Neuro-Oncology: Effective Brain Tumor Detection through Optimized Convolutional Neural Networks. IECE Transactions on Sensing, Communication, and Control, 2(1), 25–35. https://doi.org/10.62762/TSCC.2024.964451
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