logo

Brain Tumor Segmentation using Osprey Optimization Assisted DeepLabV3+ Model

Authors

  • Riya Jacob

    Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu 641114, India
  • J. Jenkin Winston

    Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu 641114, India

DOI:

https://doi.org/10.30683/1929-2279.2025.14.15

Keywords:

Brain Tumors, Segmentation, AlexNet, Enhanced DeepLabV3+, Hyperparameter Optimization, Osprey Optimization Algorithm

Abstract

Brain tumors are among the frequently diagnosed malignant conditions across all age groups. Accurately determining their grade has a major challenge for radiologists in clinical assessments and automatic diagnostic systems. Identifying tumor types and implementing preventive measures remains one of the most complex processes of brain tumor classification. Various Deep Learning (DL) models are proposed in the existing approaches for enhancing the accuracy of brain tumor classification. But, the challenges like training time and complexity are occurred in these works. To tackle these issues, this work presents a Enhanced DeepLabV3+ to segment and categorize brain tumor. At first, non-local means (NLM) filtering is utilized for pre-processing for reducing noise and preserving essential structural details. Then, the Enhanced DeepLabV3+ is employed for segmentation, with AlexNet is the backbone for segmentation tasks. To further refine the segmentation process, hyperparameter optimization of the DeepLabV3 architecture is conducted using the Osprey Optimization Algorithm (OOA) approach and provide significant improvements in brain tumor segmentation performance. The evaluation is performed on the Brain TCIA and Figshare datasets and achieved better accuracies of 98.97% and 99.23% respectively.

References

[1] Pani K, Chawla I. A hybrid approach for multi modal brain tumor segmentation using two phase transfer learning, SSL and a hybrid 3DUNET. Computers and Electrical Engineering 2024; 118: 109418.

[2] Jamazi C, Manita G, Chhabra A, Manita H, Korbaa O. Mutated Aquila Optimizer for assisting brain tumor segmentation. Biomedical Signal Processing and Control 2024; 88: 105089.

[3] An D, Liu P, Feng Y, Ding P, Zhou W, Yu B. Dynamic weighted knowledge distillation for brain tumor segmentation. Pattern Recognition 2024; 155: 110731.

[4] Jyothi P, Robert Singh A. Deep learning models and traditional automated techniques for brain tumor segmentation in MRI: a review. Artificial Intelligence Review 2023; 56(4): 2923-2969.

[5] Ranjbarzadeh R, Caputo A, Tirkolaee EB, Ghoushchi SJ, Bendechache M. Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools. Computers in Biology and Medicine 2023; 152: 106405.

[6] Zhu Z, He X, Qi G, Li Y, Cong B, Liu Y. Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI. Information Fusion 2023; 91: 376-387.

[7] Saifullah S, Dreżewski R, Yudhana A, Wielgosz M, Caesarendra W. Modified U-Net with attention gate for enhanced automated brain tumor segmentation. Neural Computing and Applications 2025; 37(7): 5521-5558. https://link.springer.com/article/10.1007/s00521-024-10919-3

[8] Qin J, Xu D, Zhang H, Xiong Z, Yuan Y, He K. BTSegDiff: Brain tumor segmentation based on multimodal MRI Dynamically guided diffusion probability model. Computers in Biology and Medicine 2025; 186: 109694.

[9] Md Islam M, Md Alamin T, Md Ashraf U, Arnisha A, Majdi K. Brainnet: Precision brain tumor classification with optimized efficientnet architecture. International Journal of Intelligent Systems 2024; 2024(1): 3583612.

[10] Jena B, Nayak GK, Saxena S. An empirical study of different machine learning techniques for brain tumor classification and subsequent segmentation using hybrid texture feature. Machine Vision and Applications 2022; 33(1): 6.

[11] Balaha HM, Hassan AS. A variate brain tumor segmentation, optimization, and recognition framework. Artificial Intelligence Review 2023; 56(7): 7403-7456.

[12] Cheng Y, Zheng Y, Wang J. CFNet: Automatic multi-modal brain tumor segmentation through hierarchical coarse-to-fine fusion and feature communication. Biomedical Signal Processing and Control 2025; 99: 106876.

[13] Mi J, Zhang X. Diffusion network with spatial channel attention infusion and frequency spatial attention for brain tumor segmentation. Medical Physics 2025; 52(1): 219-231.

[14] Cekic E, Pinar E, Pinar M, Dagcinar A. Deep learning-assisted segmentation and classification of brain tumor types on magnetic resonance and surgical microscope images. World Neurosurgery 2024; 182: e196-e204.

[15] Farnoosh R, Noushkaran H. Development of an unsupervised pseudo-deep approach for brain tumor detection in magnetic resonance images. Knowledge-Based Systems 2024; 300: 112171.

[16] Ramtekkar PK, Pandey A, Pawar MK. Innovative brain tumor detection using optimized deep learning techniques. International Journal of System Assurance Engineering and Management 2023; 14(1): 459-473.

[17] Devi RS, Perumal B, Rajasekaran MP. A hybrid deep learning based brain tumor classification and segmentation by stationary wavelet packet transform and adaptive kernel fuzzy c means clustering. Advances in Engineering Software 2022; 170: 103146.

[18] Mehnatkesh H, Jalali SMJ, Khosravi A, Nahavandi S. An intelligent driven deep residual learning framework for brain tumor classification using MRI images. Expert Systems with Applications 2023; 213: 119087.

[19] Dehghani M, Trojovský P. Osprey optimization algorithm: A new bio-inspired metaheuristic algorithm for solving engineering optimization problems. Frontiers in Mechanical Engineering 2023; 8: 1126450.

[20] Buda M, Saha A, Mazurowski MA. Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm. Computers in Biology and Medicine 2019; 109: 218-225.

[21] Deepak S, Ameer PM. Brain tumor classification using deep CNN features via transfer learning. Computers in Biology and Medicine 2019; 111: 103345.

Downloads

Published

2025-09-08

Issue

Section

Articles

How to Cite

Brain Tumor Segmentation using Osprey Optimization Assisted DeepLabV3+ Model. (2025). Journal of Cancer Research Updates, 14, 128-140. https://doi.org/10.30683/1929-2279.2025.14.15

Similar Articles

1-10 of 70

You may also start an advanced similarity search for this article.