[论文解读] Advancing Brain Tumor Detection: A Thorough Investigation of CNNs, Clustering, and SoftMax Classification in the Analysis of MRI Images

Jonayet Miah, Duc Minh Cao+4 more|arXiv (Cornell University)|Oct 26, 2023

Brain Tumor Detection and Classification被引用 8

一句话总结

本文研究基于 CNN 的 MRI 脑肿瘤检测，比较 SoftMax、RBF 与决策树分类器，并引入基于聚类的特征提取以提升准确性，SoftMax 在测试数据上达到最高 99.52%。

ABSTRACT

Brain tumors pose a significant global health challenge due to their high prevalence and mortality rates across all age groups. Detecting brain tumors at an early stage is crucial for effective treatment and patient outcomes. This study presents a comprehensive investigation into the use of Convolutional Neural Networks (CNNs) for brain tumor detection using Magnetic Resonance Imaging (MRI) images. The dataset, consisting of MRI scans from both healthy individuals and patients with brain tumors, was processed and fed into the CNN architecture. The SoftMax Fully Connected layer was employed to classify the images, achieving an accuracy of 98%. To evaluate the CNN's performance, two other classifiers, Radial Basis Function (RBF) and Decision Tree (DT), were utilized, yielding accuracy rates of 98.24% and 95.64%, respectively. The study also introduced a clustering method for feature extraction, improving CNN's accuracy. Sensitivity, Specificity, and Precision were employed alongside accuracy to comprehensively evaluate the network's performance. Notably, the SoftMax classifier demonstrated the highest accuracy among the categorizers, achieving 99.52% accuracy on test data. The presented research contributes to the growing field of deep learning in medical image analysis. The combination of CNNs and MRI data offers a promising tool for accurately detecting brain tumors, with potential implications for early diagnosis and improved patient care.

研究动机与目标

• 使用 MRI 数据促进脑肿瘤的准确早期检测。
• 评估基于 CNN 的架构在不同分类器下的 MRI 肿瘤检测。
• 评估聚类基特征提取对 CNN 性能的影响。

提出的方法

• 在健康与肿瘤患者的 MRI 扫描上训练 CNN。
• 使用 SoftMax 全连接层进行分类，并与 RBF 与决策树分类器进行比较。
• 引入聚类基特征提取方法以提升 CNN 的准确性。
• 使用准确率、灵敏度、特异性和精确度评估性能。
• 报告 SoftMax 在测试数据上达到最高准确率。

实验结果

研究问题

• RQ1 不同分类器（SoftMax、RBF、DT）的 CNN 在 MRI 图像脑肿瘤检测中的表现如何？
• RQ2 基于聚类的特征提取是否提升基于 CNN 的肿瘤分类？
• RQ3 在测试数据上哪种分类器能给出最佳的综合性能指标（准确率、灵敏度、特异性、精确度）？

主要发现

• SoftMax 分类器在训练/验证中达到 98% 的准确率，在测试数据上达到 99.52% 的准确率。
• RBF 分类器在测试数据上达到 98.24% 的准确率，而决策树在测试数据上达到 95.64% 的准确率。
• 聚类基特征提取在基线未使用聚类的 CNN 上提升了准确性。
• 研究在评估中使用灵敏度、特异性和精确度等指标与准确率共同进行评估。