利用深度学习技术对脑肿瘤进行自动评估和参数估计

B. Vijayakumari, N. Kiruthiga, C. P. Bushkala
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摘要

脑肿瘤的识别和区域提取是临床图像分析和脑相关疾病诊断的一个重要方面。从核磁共振成像图像中精确地识别肿瘤,对于有效地制定手术、放射治疗和药物治疗等治疗方案尤为重要。由于肿瘤的大小、位置和外观各不相同,因此分割是一项复杂的任务。为诊断脑肿瘤,人们创造和设计了各种分割和分类技术;然而,这些传统技术耗时长、主观性强,而且需要图像处理方面的专业知识。近来,基于深度学习的方法在脑肿瘤分割方面取得了可喜的成果。本研究旨在开发一种脑肿瘤分割和分类模型,使医疗专业人员能够准确定位和测量肿瘤,并制定有效的治疗和康复策略。这一过程包括分割肿瘤并进一步将其分为两大类型。通过对分割输出进行参数估计,可以深入了解核磁共振成像脑肿瘤的评估。随着进一步的研究和开发,基于深度学习的分割和分类可能成为准确检测和评估脑肿瘤的重要工具。基于深度学习的分割和分类方法的发展将使医学界受益匪浅,根据实验结果,所提出的框架在脑肿瘤分割和分类方面表现出色,准确率高达 99.3%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Automated evaluation and parameter estimation of brain tumor using deep learning techniques

Automated evaluation and parameter estimation of brain tumor using deep learning techniques

The identification and region extraction of brain tumors is an essential aspect of clinical image analysis and the diagnosis of brain-related illnesses. The precise and accurate identification of tumors from MRI images is particularly significant in the effective formulating of treatments such as surgery, radiation therapy, and drug therapy. The challenge of segmentation stems from the variability in the size, location, and appearance of tumors, making it a complex task. Various segmentation and classification techniques have been created and designed for brain tumor diagnosis; however, these traditional techniques are time-consuming and subjective and require expertise in image processing. In recent times, deep learning-based approaches have shown promising results in brain tumor segmentation. This research aims to develop a brain tumor segmentation and classification model that enables medical professionals to locate and measure tumors accurately and develop effective treatment and rehabilitation strategies. The process involves segmenting the tumor and further classifying it into its two major types. The parameter estimation from the segmented output provides an insight that is pivotal in the evaluation of MRI brain tumors. With further research and development, deep learning-based segmentation and classification could become an important tool for accurate detection and evaluation of brain tumors. The development of deep learning-based segmentation and classification methods can greatly benefit the medical community, and according to the finding from the experiment, it is shown that the proposed framework excels in brain tumor segmentation and classification with an accuracy of 99.3%.

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