增强脑肿瘤分类:使用 InceptionV3 和 Xception 的基于 CNN 的方法

Gawali Bhakti Shankar, Prof. V. S. Dhongade
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引用次数: 0

摘要

脑肿瘤是最具侵袭性和致命性的疾病之一,最高级别的脑肿瘤患者寿命极短。为应对这一问题,早期发现和治疗至关重要。人工调查脑肿瘤分类是一项耗时的任务,而且可能存在人为错误。因此,在短时间内进行准确分析至关重要。本方法采用高精度卷积神经网络(CNN)、InceptionV3 和 Xception 算法,对胶质瘤、脑膜瘤和无肿瘤进行自动脑肿瘤分类。大脑部分最初采用阈值法进行分割,然后进行形态学操作。使用 CNN、Inceptionv3 和 Xceptionv3 算法对大脑 MRI 进行分类。系统的性能使用精确度、召回率、F1 分数和准确度参数进行评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing Brain Tumor Classification: A CNN-Based Approach with InceptionV3 and Xception
Brain tumors are among the most aggressive and deadly diseases, with a very short life expectancy at the highest grade. To combat this, early detection and treatment are crucial.In this approach, MRI images are used to analyze brain abnormalities. The manual investigation of brain tumor classification is a time-consuming task, and there might be possibilities of human errors. Hence, accurate analysis in a short span of time is essential. This approach presents the automatic brain tumor classification algorithm using a highly accurate Convolutional Neural Network (CNN), InceptionV3 and Xception algorithm for classification of Glioma, Meningioma and No tumor. The brain part is initially segmented by a thresholding approach followed by a morphological operation. The brain MRI is classified using CNN, Inceptionv3, and Xceptionv3 algorithms. The system's performance is evaluated using precision, recall, F1 score, and accuracy parameters.
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