Effective Approach Based on Slime Mold Algorithm for Hyper-Parameter Tuning of CNNs in Brain Tumor Classification

Kevser Özdem Karaca; Bilgehan Arslan; Yılmaz Atay; Seref Sagiroglu

doi:10.54287/gujsa.1822726

Effective Approach Based on Slime Mold Algorithm for Hyper-Parameter Tuning of CNNs in Brain Tumor Classification

Abstract

Research on brain cancer indicates that the severity of the disease varies according to tumor types and their specific characteristics. Accurate identification of tumor locations, extraction of distinctive features, and correct classification are of critical importance. In this study, brain tumors were detected using three different deep learning models developed on a dataset created within the scope of the TBP project and seven additional public datasets. All three models are based on Convolutional Neural Network (CNN) architectures for tumor detection. The first model is a baseline CNN; the second incorporates the Genetic Algorithm (GA), a traditional approach for hyperparameter optimization; and the third combines the CNN with the Slime Mold Algorithm (SMA), a recently proposed metaheuristic technique. Hybrid methods that achieve higher performance than baseline CNNs in binary tumor classification are presented and discussed. Experimental results were comparatively analyzed and visualized through graphs and tables. Compared to other CNN-based studies in the literature, the proposed approach improved accuracy by approximately 1–10%. Similarly, when compared with other machine learning (ML) and deep learning (DL) algorithms, excluding CNNs, performance gains ranged between 1% and 13%. The CNN+SMA model achieved the most consistent and notable improvements across all datasets. Although hybrid models generally require more computational resources due to their complex training structures, they tend to achieve higher accuracy and facilitate parameter optimization more effectively than single-model approaches.

Keywords

Supporting Institution

This work was carried out within the scope of the Turkish Brain Project (No. 2021-001), supported by the Turkish Presidency Digital Transformation Office (TR-DTO). The authors thank TR-DTO for financial support and Gazi University Medical School Hospital and the Gazi AI R&D Center for providing research facilities. The authors also appreciate the contributions of colleagues from TR-DTO for preliminary infrastructure support.

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Details

Primary Language

English

Subjects

Deep Learning, Satisfiability and Optimisation

Journal Section

Research Article

Authors

Kevser Özdem Karaca
0000-0002-6695-200X
Türkiye

Bilgehan Arslan ^*
0000-0002-5160-4408
Türkiye

Yılmaz Atay
0000-0002-3298-3334
Türkiye

Seref Sagiroglu
0000-0003-0805-5818
Türkiye

Publication Date

December 31, 2025

Submission Date

November 13, 2025

Acceptance Date

December 15, 2025

Published in Issue

Year 2025 Volume: 12 Number: 4

DOI

https://doi.org/10.54287/gujsa.1822726

IZ

https://izlik.org/JA26CR39DA

Cite

RIS / Bibtex

APA

Özdem Karaca, K., Arslan, B., Atay, Y., & Sagiroglu, S. (2025). Effective Approach Based on Slime Mold Algorithm for Hyper-Parameter Tuning of CNNs in Brain Tumor Classification. Gazi University Journal of Science Part A: Engineering and Innovation, 12(4), 1187-1216. https://doi.org/10.54287/gujsa.1822726

AMA

1.Özdem Karaca K, Arslan B, Atay Y, Sagiroglu S. Effective Approach Based on Slime Mold Algorithm for Hyper-Parameter Tuning of CNNs in Brain Tumor Classification. GU J Sci, Part A. 2025;12(4):1187-1216. doi:10.54287/gujsa.1822726

Chicago

Özdem Karaca, Kevser, Bilgehan Arslan, Yılmaz Atay, and Seref Sagiroglu. 2025. “Effective Approach Based on Slime Mold Algorithm for Hyper-Parameter Tuning of CNNs in Brain Tumor Classification”. Gazi University Journal of Science Part A: Engineering and Innovation 12 (4): 1187-1216. https://doi.org/10.54287/gujsa.1822726.

EndNote

Özdem Karaca K, Arslan B, Atay Y, Sagiroglu S (December 1, 2025) Effective Approach Based on Slime Mold Algorithm for Hyper-Parameter Tuning of CNNs in Brain Tumor Classification. Gazi University Journal of Science Part A: Engineering and Innovation 12 4 1187–1216.

IEEE

[1]K. Özdem Karaca, B. Arslan, Y. Atay, and S. Sagiroglu, “Effective Approach Based on Slime Mold Algorithm for Hyper-Parameter Tuning of CNNs in Brain Tumor Classification”, GU J Sci, Part A, vol. 12, no. 4, pp. 1187–1216, Dec. 2025, doi: 10.54287/gujsa.1822726.

ISNAD

Özdem Karaca, Kevser - Arslan, Bilgehan - Atay, Yılmaz - Sagiroglu, Seref. “Effective Approach Based on Slime Mold Algorithm for Hyper-Parameter Tuning of CNNs in Brain Tumor Classification”. Gazi University Journal of Science Part A: Engineering and Innovation 12/4 (December 1, 2025): 1187-1216. https://doi.org/10.54287/gujsa.1822726.

JAMA

1.Özdem Karaca K, Arslan B, Atay Y, Sagiroglu S. Effective Approach Based on Slime Mold Algorithm for Hyper-Parameter Tuning of CNNs in Brain Tumor Classification. GU J Sci, Part A. 2025;12:1187–1216.

MLA

Özdem Karaca, Kevser, et al. “Effective Approach Based on Slime Mold Algorithm for Hyper-Parameter Tuning of CNNs in Brain Tumor Classification”. Gazi University Journal of Science Part A: Engineering and Innovation, vol. 12, no. 4, Dec. 2025, pp. 1187-16, doi:10.54287/gujsa.1822726.

Vancouver

1.Kevser Özdem Karaca, Bilgehan Arslan, Yılmaz Atay, Seref Sagiroglu. Effective Approach Based on Slime Mold Algorithm for Hyper-Parameter Tuning of CNNs in Brain Tumor Classification. GU J Sci, Part A. 2025 Dec. 1;12(4):1187-216. doi:10.54287/gujsa.1822726