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.

翻译：本研究系统探讨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，重点分析直方图均衡化（HE）、对比度受限自适应直方图均衡化（CLAHE）及其混合变体。采用U-Net架构在3064张脑MRI图像数据集上进行实验，深入研究包括尺寸调整与图像增强在内的预处理步骤，以优化分割精度。本文详细阐述了基于CNN的U-Net架构、训练及验证流程。通过准确率、损失值、均方误差、交并比（IoU）和Dice系数等指标的对比分析表明，混合方法CLAHE-HE始终优于其他方法。结果凸显其优越性能：训练、测试和验证阶段的准确率分别为0.9982、0.9939、0.9936，Jaccard指数分别为0.9862、0.9847、0.9864，Dice系数分别为0.993、0.9923、0.9932，充分彰显其在神经肿瘤学应用中的潜力。本研究最后呼吁进一步改进分割方法，以提升神经肿瘤学诊断精度和治疗规划的准确性。