Deep Learning-Based Cancer Diagnosis Using a Bluefin Trevally-Optimized Convolutional Neural Network

A. Kaliappan; Subhasini Shukla; Rahul Nandkumar Khadke; Ganesh B. Dongre; Robin George; Nitesh N. Nikam; R. D. Prathibha; M. R. Amruthalakshmi; B. Aruna; Madhumathi R.; Awwab Mohammad

doi:10.48084/etasr.17713

Authors

A. Kaliappan School of Computing, SRM Institute of Science and Technology, Tiruchirappalli, Tamil Nadu, India
Subhasini Shukla Electronics & Computer Science Engineering, St. John College of Engineering & Management, Palghar, Maharashtra, India
Rahul Nandkumar Khadke Department of Electronics & Telecommunication, Aditya Engineering College, Beed, Maharashtra, India
Ganesh B. Dongre Department of Electronics and Computer Engineering, CSMSS Chh. Shahu College of Engineering, Chhatrapati Sambhajinagar, MH, India
Robin George Institute of Science and Technology, Chinmaya Vishwa Vidyapeeth, Ernakulam, Kerala, India
Nitesh N. Nikam Department of Electrical Engineering, CSMSS Chh. Shahu College of Engineering, Chhatrapati Sambhajinagar, MH, India
R. D. Prathibha Department of Zoology, St. Joseph's University, Bengaluru, India
M. R. Amruthalakshmi Department of Mathematics, Dayananda Sagar College of Engineering, Bengaluru, India
B. Aruna Department of Electronics and Communication Engineering, BGS Institute of Technology, Adichunchanagiri University, B G Nagara, Mandya, Karnataka, India
Madhumathi R. Department of Computer Science and Engineering, Sri Ramakrishna Engineering College, Coimbatore, India
Awwab Mohammad Department of Computer Science and Technology, Manav Rachna University, Faridabad, India

Volume: 16 | Issue: 2 | Pages: 33979-33984 | April 2026 | https://doi.org/10.48084/etasr.17713

Received: 22 January 2026 | Revised: 7 February 2026 and 18 February 2026 | Accepted: 19 February 2026 | Online: 9 March 2026

Corresponding author: Awwab Mohammad

Abstract

Accurate and automated detection of brain tumors from Magnetic Resonance Imaging (MRI) scans is critical for effective clinical diagnosis and treatment planning. This paper proposes a Bluefin Trevally Optimization-based Deep Learning (BTO-DL) framework for robust brain tumor detection and analysis from MRI images. The proposed approach integrates a Convolutional Neural Network (CNN)–based feature extraction with a bio-inspired Bluefin Trevally Optimization (BTO) algorithm to jointly optimize network hyperparameters and feature importance weights, thereby enhancing convergence stability and generalization performance. The framework was tested on several publicly available benchmark brain MRI datasets, such as BraTS, Figshare, and Kaggle. It was trained over 50 epochs and used a patient-independent data split, demonstrating superior classification accuracy, spatial overlap, and convergence stability compared to existing methods. On the BraTS dataset, it achieves a classification accuracy of over 98%, Receiver Operating Characteristic–Area Under the Curve (ROC–AUC) values close to 0.99, and a Dice coefficient of 0.91. The model also shows stable training convergence, strong cross-dataset generalization, and low inference latency of about 18.6 ms per MRI slice indicating suitability for real-time clinical applications. A statistical significance test shows that the observed performance improvements are statistically significant (p < 0.01). The proposed BTO-DL framework offers a precise, efficient, and resilient solution for the automated detection and localization of brain tumors from MRI scans.

Keywords:

brain tumor detection, MRI, deep learning, Bluefin Trevally Optimization (BTO), Convolutional Neural Network (CNN), Dice coefficient

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