Optimizing Brain Tumor Segmentation Through CNN U-Net with CLAHE-HE Image Enhancement

DOI

10.2991/978-94-6463-366-5_9How to use a DOI?

Keywords

Brain Tumor Segmentation; CNN U-Net; CLAHE-HE Enhancement; Medical Image Analysis; Deep Learning in Biomedical Imaging

Abstract

Accurate segmentation of brain tumors in medical images is paramount for precise diagnosis and treatment planning. In this study, we introduce a robust approach for brain tumor segmentation employing Convolutional Neural Networks (CNNs) with Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Histogram Equalization (HE) preprocessing techniques. We leverage the CNN U-Net architecture, enhanced with CLAHE-HE preprocessing, to achieve high precision in brain tumor segmentation. Our evaluation demonstrates the effectiveness of this approach, revealing substantial improvements in accuracy (reaching up to 0.9982), loss (reducing to 0.0054), Mean Squared Error (MSE, decreasing to 0.0015), Intersection over Union (IoU, increase up to 0.9953), and Dice Score (increase up to 0.9977) during training, validation, and testing phases. Notably, the capacity of our model to generalize effectively is evident through the close alignment of validation performance with training results. These findings underscore the potential of preprocessing techniques in enhancing medical image analysis, with the proposed approach showcasing the promise of revolutionizing brain tumor segmentation, thus contributing to more accurate and reliable diagnoses in clinical settings. Future works may explore innovative preprocessing methods and the application of the proposed approach to other medical image segmentation tasks, which will further advance its capabilities and possible applications areas.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Shoffan Saifullah
AU  - Andiko Putro Suryotomo
AU  - Rafał Dreżewski
AU  - Radius Tanone
AU  - Tundo Tundo
PY  - 2024
DA  - 2024/02/02
TI  - Optimizing Brain Tumor Segmentation Through CNN U-Net with CLAHE-HE Image Enhancement
BT  - Proceedings of the 2023 1st International Conference on Advanced Informatics and Intelligent Information Systems (ICAI3S 2023)
PB  - Atlantis Press
SP  - 90
EP  - 101
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-366-5_9
DO  - 10.2991/978-94-6463-366-5_9
ID  - Saifullah2024
ER  -