Advancing multi-categorization and segmentation in brain tumors using novel efficient deep learning approaches.

IF 3.5 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
PeerJ Computer Science Pub Date : 2024-11-29 eCollection Date: 2024-01-01 DOI:10.7717/peerj-cs.2496
Nadenlla RajamohanReddy, G Muneeswari
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引用次数: 0

Abstract

Background: A brain tumor is the development of abnormal brain cells, some of which may progress to cancer. Early identification of illnesses and development of treatment plans improve patients' quality of life and life expectancy. Brain tumors are most commonly detected by magnetic resonance imaging (MRI) scans. The range of tumor sizes, shapes, and locations in the brain makes the existing approaches inadequate for accurate classification. Furthermore, using the current model takes a lot of time and yields results that are not as accurate. The primary goal of the suggested approach is to categorize whether a brain tumor is present, identify its type and divide the affected area into segments.

Methods: Therefore, this research introduced a novel efficient DL-based extension residual structure and adaptive channel attention mechanism (ERSACA-Net) to classify the brain tumor types as pituitary, glioma, meningioma and no tumor. Extracting features in brain tumor analysis helps in accurately characterizing tumor properties, which aids in precise diagnosis, treatment planning, and monitoring of disease progression. For this purpose, we utilized Enhanced Res2Net to extract the essential features. Using the Binary Chaotic Transient Search Optimization (BCTSO) Algorithm, the most pertinent features in terms of shape, texture, and colour are chosen to minimize complexity.

Results: Finally, a novel LWIFCM_CSA approach is introduced, which is the ensemble of Local-information weighted intuitionistic Fuzzy C-means clustering algorithm (LWIFCM) and Chameleon Swarm Algorithm (CSA). Conditional Tabular Generative Adversarial Network (CTGAN) is used to tackle class imbalance problems. While differentiating from existing approaches, the proposed approach gains a greater solution. This stable improvement in accuracy highlights the suggested classifier's strong performance and raises the possibility of more precise and trustworthy brain tumor classification. In addition, our method's processing time, which averaged 0.11 s, was significantly faster than that of previous approaches.

利用新颖高效的深度学习方法推进脑肿瘤的多分类和分割。
背景:脑肿瘤是异常脑细胞的发展,其中一些可能发展为癌症。疾病的早期识别和治疗计划的制定可以改善患者的生活质量和预期寿命。脑肿瘤最常用的检测方法是磁共振成像(MRI)扫描。肿瘤的大小、形状和在大脑中的位置的范围使得现有的方法不足以准确分类。此外,使用当前的模型需要花费大量时间,并且产生的结果不那么准确。建议的方法的主要目标是对脑肿瘤是否存在进行分类,确定其类型并将受影响的区域划分为部分。方法:为此,本研究引入了一种新的高效的基于dl的延伸残差结构和自适应通道注意机制(ERSACA-Net),将脑肿瘤分为垂体、胶质瘤、脑膜瘤和无瘤。在脑肿瘤分析中提取特征有助于准确表征肿瘤特性,从而有助于精确诊断、治疗计划和监测疾病进展。为此,我们使用Enhanced Res2Net来提取基本特征。利用二元混沌瞬态搜索优化算法(BCTSO),在形状、纹理和颜色方面选择最相关的特征以最小化复杂性。结果:最后,提出了一种新的LWIFCM_CSA方法,该方法是局部信息加权直觉模糊c均值聚类算法(LWIFCM)和变色龙群算法(CSA)的集成。条件表格生成对抗网络(Conditional Tabular Generative Adversarial Network,简称CTGAN)用于解决职业不平衡问题。虽然与现有方法有所不同,但所提出的方法获得了更大的解决方案。准确度的稳定提高凸显了所建议分类器的强大性能,并提高了更精确和可信的脑肿瘤分类的可能性。此外,该方法的平均处理时间为0.11 s,明显快于以往的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
PeerJ Computer Science
PeerJ Computer Science Computer Science-General Computer Science
CiteScore
6.10
自引率
5.30%
发文量
332
审稿时长
10 weeks
期刊介绍: PeerJ Computer Science is the new open access journal covering all subject areas in computer science, with the backing of a prestigious advisory board and more than 300 academic editors.
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