Yazar "Kilicarslan, Gulhan" seçeneğine göre listele

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    Dense-MoE vs Lite-MoE: A Gating-Weight-Aware Pruning Framework for Unpaired Multimodal Breast Cancer Diagnosis
    (Springer, 2026) Cetintas, Dilber; Tuncer, Taner; Kilicarslan, Gulhan; Sevi, Mehmet
    This study proposes a unique Mixture-of-Experts (MoE)-based deep learning framework for the effective use of unpaired multimodal images in breast cancer diagnosis. Mammography (MG), ultrasonography (US), and magnetic resonance imaging (MRI) data are modeled as independent expert networks based on DenseNet-121, and a Gating Network mechanism is integrated to dynamically determine the contribution of each modality in the decision-making process. Thus, the model offers a flexible and clinically relevant structure without requiring all modalities to be available in every patient. The unique contribution of the study, the Gating-Weight-Aware Selection strategy, performs pruning by considering not only the individual Area Under the Curve (AUC) performance of the experts but also their usage rate within the model. Experimental results show that the Dense-MoE model achieves an AUC of 0.9661 and 89% accuracy. The pruned Lite-MoE model, despite reducing the number of parameters by approximately 33%, largely maintains its performance with an AUC of 0.9529 and 88% accuracy. Clinical evaluations of the proposed model were examined using Gradient-weighted Class Activation Mapping (GradCAM) heatmaps. The results showed that the model has the potential to be a flexible, scalable, and high-performance clinical decision support system in scenarios where data is incomplete.
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    Fusion-Based Deep Learning Approach for Renal Cell Carcinoma Subtype Detection Using Multi-Phasic MRI Data
    (Mdpi, 2025) Kilicarslan, Gulhan; Cetintas, Dilber; Tuncer, Taner; Yildirim, Muhammed
    Background/Objectives: Renal cell carcinoma (RCC) is a malignant disease that requires rapid and reliable diagnosis to determine the correct treatment protocol and to manage the disease effectively. However, the fact that the textural and morphological features obtained from medical images do not differ even among different tumor types poses a significant diagnostic challenge for radiologists. In addition, the subjective nature of visual assessments made by experts and interobserver variability may cause uncertainties in the diagnostic process. Methods: In this study, a deep learning-based hybrid model using multiphase magnetic resonance imaging (MRI) data is proposed to provide accurate classification of RCC subtypes and to provide a decision support mechanism to radiologists. The proposed model performs a more comprehensive analysis by combining the T2 phase obtained before the administration of contrast material with the arterial (A) and venous (V) phases recorded after the injection of contrast material. Results: The model performs RCC subtype classification at the end of a five-step process. These are regions of interest (ROI), preprocessing, augmentation, feature extraction, and classification. A total of 1275 MRI images from different phases were classified with SVM, and 90% accuracy was achieved. Conclusions: The findings reveal that the integration of multiphase MRI data and deep learning-based models can provide a significant improvement in RCC subtype classification and contribute to clinical decision support processes.