  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.
