A HYBRID CNN-LSTM APPROACH FOR BRAIN TUMOR CLASSIFICATION: A COMPARATIVE PERFORMANCE ANALYSIS WITH CONVENTIONAL CLASSIFIERS

Hicran Güneş; Cemal Aktürk; Tarık Talan

doi:10.17780/ksujes.1638455

EN TR

A HYBRID CNN-LSTM APPROACH FOR BRAIN TUMOR CLASSIFICATION: A COMPARATIVE PERFORMANCE ANALYSIS WITH CONVENTIONAL CLASSIFIERS

Abstract

Early diagnosis of brain tumors is vital for improving patients' quality of life and optimizing treatment processes. This study proposes a novel model combining CNN (Convolutional Neural Network)-based deep feature extraction and LSTM (Long Short Term Memory) networks in a hybrid structure for the classification of brain tumors. The CNN used for deep feature extraction processes spatial information obtained from Magnetic Resonance Imaging (MRI) images, while the LSTM network has the ability to analyze this information through sequential patterns. The performance of the developed hybrid model is evaluated comparatively with ANN (Artificial Neural Networks), KNN (K-Nearest Neighbor), SVM (Support Vector Machines), and Random Forest classifiers. Experimental studies show that the proposed CNN-LSTM model outperforms other methods with 97.15% accuracy. The findings show that the sequential data processing capability of the LSTM model increases image classification performance when used as a hybrid with CNN-based features, demonstrating the potential of hybrid architectures and providing a powerful and innovative solution that can be used especially in the field of medical imaging.

Keywords

References

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Details

Primary Language

English

Subjects

Image Processing , Deep Learning

Journal Section

Research Article

Authors

Hicran Güneş
0000-0003-4626-1874
Türkiye

Cemal Aktürk ^*
0000-0003-3764-3862
Türkiye

Tarık Talan
0000-0002-5371-4520
Türkiye

Publication Date

September 3, 2025

Submission Date

February 12, 2025

Acceptance Date

August 8, 2025

Published in Issue

Year 1970 Volume: 28 Number: 3

DOI

https://doi.org/10.17780/ksujes.1638455

IZ

https://izlik.org/JA43MU62JT

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RIS / Bibtex

APA

Güneş, H., Aktürk, C., & Talan, T. (2025). A HYBRID CNN-LSTM APPROACH FOR BRAIN TUMOR CLASSIFICATION: A COMPARATIVE PERFORMANCE ANALYSIS WITH CONVENTIONAL CLASSIFIERS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(3), 1219-1233. https://doi.org/10.17780/ksujes.1638455

A HYBRID CNN-LSTM APPROACH FOR BRAIN TUMOR CLASSIFICATION: A COMPARATIVE PERFORMANCE ANALYSIS WITH CONVENTIONAL CLASSIFIERS

A HYBRID CNN-LSTM APPROACH FOR BRAIN TUMOR CLASSIFICATION: A COMPARATIVE PERFORMANCE ANALYSIS WITH CONVENTIONAL CLASSIFIERS

Abstract

Keywords

BEYİN TÜMÖRÜ SINIFLANDIRMASINDA CNN-LSTM HİBRİT YAKLAŞIMI VE GELENEKSEL SINIFLANDIRICILARLA KARŞILAŞTIRMALI PERFORMANS ANALİZİ

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Anahtar Kelimeler

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