Comparative Analysis of Image Enhancement Techniques for Brain Tumor Segmentation: Contrast, Histogram, and Hybrid Approaches

arXiv:2404.05341v1 Announce Type: cross
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 …

abstract analysis architecture arxiv brain cnn comparative analysis contrast convolutional neural network cs.cv dataset eess.iv equalization hybrid image impact network neural network segmentation study type