INDEREX-NET: AN ATTENTION-GUIDED MULTI-STREAM CNN FRAMEWORK WITH ADAPTIVE FEATURE FUSION FOR BRAIN TUMOR CLASSIFICATION

Abstract

Accurate classification of brain tumors from magnetic resonance imaging (MRI) data remains a challenging problem for computer-aided diagnosis systems due to inter-class similarity, intra-class variability, and heterogeneous tumor morphology. In this study, an attention-guided multi-stream deep learning architecture, termed IndereX-Net, is proposed for brain tumor classification from axial MRI images. The proposed model integrates three heterogeneous ImageNet-pretrained convolutional neural network backbones, namely ResNet50, DenseNet121, and InceptionV3, arranged in parallel streams to extract complementary multi-scale feature representations. Each stream is enhanced with a Convolutional Block Attention Module (CBAM), while Generalized Mean (GeM) pooling is employed for adaptive feature aggregation. In addition, a gated feature fusion mechanism is incorporated to dynamically regulate inter-stream interactions. The model is trained using a two-phase transfer learning strategy and evaluated on a publicly available brain tumor MRI dataset. Experimental results demonstrate that IndereX-Net outperforms individual backbone models, achieving an accuracy of 98.22% and a ROC-AUC of 0.9986, indicating that attention-guided multi-stream architectures provide an effective and reliable solution for computer-aided brain tumor diagnosis.

Keywords

• Brain Tumor Classification
• Medical Image Analysis
• Attention-Based Deep Learning
• Multi-Stream Convolutional Neural Networks
• Adaptive Feature Fusion

INDEREX-NET: BEYİN TÜMÖRÜ SINIFLANDIRMASI İÇİN UYARLANABİLİR ÖZELLİK FÜZYONUNA SAHİP DİKKAT TABANLI ÇOK AKIŞLI CNN MİMARİSİ

Abstract

Manyetik rezonans görüntüleme (MRI) verilerinden beyin tümörlerinin doğru sınıflandırılması; sınıflar arası benzerlik, sınıf içi değişkenlik ve tümör morfolojisindeki heterojenlik nedeniyle bilgisayar destekli tanı sistemleri açısından zorlu bir problemdir. Bu çalışmada, aksiyal MRI görüntülerinden beyin tümörü sınıflandırması için IndereX-Net adı verilen, dikkat mekanizması ile yönlendirilmiş çok akışlı bir derin öğrenme mimarisi önerilmektedir. Önerilen model; ResNet50, DenseNet121 ve InceptionV3 olmak üzere, ImageNet üzerinde önceden eğitilmiş üç heterojen evrişimsel sinir ağı omurgasını paralel akışlar halinde bütünleştirerek tamamlayıcı çok ölçekli özellik temsilleri elde etmektedir. Her bir akış Convolutional Block Attention Module (CBAM) ile güçlendirilmiş, uyarlanabilir özellik birleştirme için Generalized Mean (GeM) pooling kullanılmıştır. Ayrıca, akışlar arası etkileşimi dinamik olarak düzenleyen kapılı özellik füzyon mekanizması mimariye entegre edilmiştir. Model, iki aşamalı transfer öğrenme stratejisi ile eğitilmiş ve herkese açık bir beyin tümörü MRI veri seti üzerinde değerlendirilmiştir. Deneysel sonuçlar, IndereX-Net’in tekil omurga modellerine kıyasla daha üstün performans sergilediğini; %98,22 doğruluk ve 0,9986 ROC-AUC değerlerine ulaştığını göstererek, dikkat destekli çok akışlı mimarilerin bilgisayar destekli beyin tümörü tanısı için etkili ve güvenilir bir çözüm sunduğunu ortaya koymaktadır.

Keywords

• Beyin Tümörü Sınıflandırması
• Tıbbi Görüntü Analizi
• Dikkat Tabanlı Derin Öğrenme
• Çok Akışlı Evrişimsel Sinir Ağları
• Uyarlanabilir Özellik Füzyonu

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