Abstract
This study systematically investigates the impact of image enhancement techniques on Convolutional Neural Network (CNN)-based Brain Tumor Segmentation, focusing on Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and their hybrid variations. Employing the U-Net architecture on a dataset of 3064 Brain MRI images, the research delves into preprocessing steps, including resizing and enhancement, to optimize segmentation accuracy. A detailed analysis of the CNN-based U-Net architecture, training, and validation processes is provided. The comparative analysis, utilizing metrics such as Accuracy, Loss, MSE, IoU, and DSC, reveals that the hybrid approach CLAHE-HE consistently outperforms others. Results highlight its superior accuracy (0.9982, 0.9939, 0.9936 for training, testing, and validation, respectively) and robust segmentation overlap, with Jaccard values of 0.9862, 0.9847, and 0.9864, and Dice values of 0.993, 0.9923, and 0.9932 for the same phases, emphasizing its potential in neuro-oncological applications. The study concludes with a call for refinement in segmentation methodologies to further enhance diagnostic precision and treatment planning in neuro-oncology.
Abstract (translated)
本研究系统地研究了图像增强技术对基于卷积神经网络（CNN）的脑肿瘤分割的影响，重点关注归一化等价（HE）、对比有限适应性归一化（CLAHE）及其混合变体。在包含3064个脑部MRI图像的数据集上应用U-Net架构，研究深入探讨了预处理步骤，包括缩放和增强，以优化分割准确性。提供了基于CNN的U-Net架构、训练和验证过程的详细分析。比较分析使用了诸如准确度、损失、均方误差（MSE）、IoU和DSC等指标，显示了混合方法CLAHE-HE始终优于其他方法。结果突出了其卓越的准确性（分别为训练、测试和验证的0.9982、0.9939和0.9936），以及稳健的分割重叠，以及 Jaccard 值为0.9862、0.9847 和0.9864，以及IoU值为0.993、0.9923 和0.9932 的相同阶段。研究强调了其在神经肿瘤学应用中的潜在价值。研究结论呼吁在分割方法上进行优化，以进一步提高神经肿瘤学诊断的准确性和治疗规划。
URL
https://arxiv.org/abs/2404.05341
