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

arXiv: https://arxiv.org/abs/2404.05341 · HTML: https://mlsanity.com/paper/2404.05341/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 52 references that are in the map.
- First posted on arXiv 8 Apr 2024.

## Builds on (most cited first, 7 of 7)

- [TransBTS: Multimodal Brain Tumor Segmentation Using Transformer](https://mlsanity.com/paper/2103.04430/) (2021)
- [Deep Learning Based Brain Tumor Segmentation: A Survey](https://mlsanity.com/paper/2007.09479/) (2020)
- [Zero-shot performance of the Segment Anything Model (SAM) in 2D medical imaging: A comprehensive evaluation and practical guidelines](https://mlsanity.com/paper/2305.00109/) (2023)
- [CNN Feature Map Augmentation for Single-Source Domain Generalization](https://mlsanity.com/paper/2305.16746/) (2023)
- [Segmentasi Citra Menggunakan Metode Watershed Transform Berdasarkan Image Enhancement Dalam Mendeteksi Embrio Telur](https://mlsanity.com/paper/2102.04202/) (2021)
- [Spherical coordinates transformation pre-processing in Deep Convolution Neural Networks for brain tumor segmentation in MRI](https://mlsanity.com/paper/2008.07090/) (2020)
- [Nondestructive chicken egg fertility detection using CNN-transfer learning algorithms](https://mlsanity.com/paper/2309.16257/) (2023)

## Abstract (arXiv)

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.