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

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Görüntü İşleme , Derin Öğrenme

Bölüm

Araştırma Makalesi

Yazarlar

Hicran Güneş
0000-0003-4626-1874
Türkiye

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

Tarık Talan
0000-0002-5371-4520
Türkiye

Yayımlanma Tarihi

3 Eylül 2025

Gönderilme Tarihi

12 Şubat 2025

Kabul Tarihi

8 Ağustos 2025

Yayımlandığı Sayı

Yıl 1970 Cilt: 28 Sayı: 3

DOI

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

IZ

https://izlik.org/JA43MU62JT

Kaynak Göster

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

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

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

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster