磁共振成像中的智能多级脑肿瘤识别:基于元搜索的不确定集合框架

Saravanan Alagarsamy;Vishnuvarthanan Govindaraj;A. Shahina;D. Nagarajan
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引用次数: 0

摘要

这项研究旨在通过开发一种结合了萤火虫(FF)算法和区间II型模糊(IT2FLS)技术的自动化方法,满足精确预测脑肿瘤的迫切需要。所提出的方法利用萤火虫算法寻找可能的聚类位置,并利用 IT2FLS 系统进行最终聚类,从而改进了复杂脑组织中的肿瘤划分。该算法通过处理来自 BRATS 挑战数据集(2017 年、2018 年和 2020 年)的各种图像序列,展示了其多功能性,这些数据集包含不同程度的复杂性。通过灵敏度、特异性和骰子重叠指数(DOI)等综合评估指标,所提出的算法始终能产生更好的分割结果。最终,这项研究旨在增强肿瘤学家的感知敏锐度,促进对患者病情的直觉和理解,从而提高医学研究的决策能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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CiteScore
7.70
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