Comparative analysis of image enhancement techniques for braintumor segmentation: contrast, histogram, and hybrid approaches

Comparative analysis of image enhancement techniques for braintumor segmentation: contrast, histogram, and hybrid approaches

Blog Article

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 PLANT ENZYMES (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 SERVERS 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.