Intelligent Multigrade Brain Tumor Identification in MRI: A Metaheuristic-Based Uncertain Set Framework

This research intends to address the critical need for precise brain tumor prediction through the development of an automated method that entwines the Firefly (FF) algorithm and the interval type-II fuzzy (IT2FLS) technique. The proposed method improves tumor delineation in complex brain tissue by using the FF algorithm to find possible cluster positions and the IT2FLS system for final clustering. This algorithm demonstrates its versatility by processing diverse image sequences from BRATS challenge datasets (2017, 2018, and 2020), which encompass varying levels of complexity. Through comprehensive evaluation metrics such as sensitivity, specificity, and dice-overlap index (DOI), the proposed algorithm consistently yields improved segmentation results. Ultimately, this research aims to augment oncologists' perceptual acumen, facilitating enhanced intuition and comprehension of patients' conditions, thereby advancing decision-making capabilities in medical research.

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Publication Details

Journal
IEEE Transactions on Artificial Intelligence
Published
2024-08-12
DOI
https://doi.org/10.1109/tai.2024.3441520
Citations
14
Primary Topic
Brain Tumor Detection and Classification
Type
article
Field-Weighted Citation Impact
3.46
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article

Intelligent Multigrade Brain Tumor Identification in MRI: A Metaheuristic-Based Uncertain Set Framework

Divyashri Ramanathan Nagarajan, A. Shahina, Saravanan Alagarsamy, Vishnuvarthanan Govindaraj
14 citations
IEEE Transactions on Artificial Intelligence
Brain Tumor Detection and Classification
3.46
article

Intelligent Multigrade Brain Tumor Identification in MRI: A Metaheuristic-Based Uncertain Set Framework

Divyashri Ramanathan Nagarajan, A. Shahina, Saravanan Alagarsamy, Vishnuvarthanan Govindaraj
article en
14 citations

Abstract

This research intends to address the critical need for precise brain tumor prediction through the development of an automated method that entwines the Firefly (FF) algorithm and the interval type-II fuzzy (IT2FLS) technique. The proposed method improves tumor delineation in complex brain tissue by using the FF algorithm to find possible cluster positions and the IT2FLS system for final clustering. This algorithm demonstrates its versatility by processing diverse image sequences from BRATS challenge datasets (2017, 2018, and 2020), which encompass varying levels of complexity. Through comprehensive evaluation metrics such as sensitivity, specificity, and dice-overlap index (DOI), the proposed algorithm consistently yields improved segmentation results. Ultimately, this research aims to augment oncologists' perceptual acumen, facilitating enhanced intuition and comprehension of patients' conditions, thereby advancing decision-making capabilities in medical research.

IEEE Transactions on Artificial IntelligenceVol. 5(11)
Barkatullah University (IN), Sri Sivasubramaniya Nadar College of Engineering (IN)
Openalex Percentile: Top 7%
Brain Tumor Detection and Classification
3.46
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Intelligent Multigrade Brain Tumor Identification in MRI: A Metaheuristic-Based Uncertain Set Framework — Divyashri Ramanathan Nagarajan, A. Shahina, et al. · IEEE Transactions on Artificial Intelligence (2024) | TGRS Research Map | TGRS