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- MakaleYalnız künye2026Multi-scale deep fusion architecture for brain tumor detection via local-contextual feature integration
Reis, Hatice Catal; Turk, Veysel
Early detection of brain tumors is crucial for enhancing treatment strategies and improving clinical prognosis. However, current conventional methods limit diagnostic accuracy and slow down diagnostic processes due to subjective interpretations and dependence on experts. Deep learning-based approaches have the potential to enhance diagnostic accuracy and facilitate early diagnosis by mitigating current limitations in automatic learning techniques for feature extraction and modeling processes. In this study, an advanced deep learning model, Multi-Scale Local-Context Fusion Network (MSLCFNet), is proposed for automatic diagnosis of brain tumors within the scope of oncological diseases. The proposed model evaluates local spatial features, global patterns, and long-range contextual relationships holistically through multi-layered representations, enabling effective identification of complex structures of tumor morphology. This architecture has the potential to increase diagnostic consistency and minimize the risk of misdiagnosis by reducing inter-observer variability, especially in cases where limited contrast and unclear boundaries lead to diagnostic uncertainty. The proposed model achieved accuracy rates of 96.51% and 98.17% on two different MRI brain tumor datasets, respectively. Additionally, a hybrid model based on MSLCFNet+ResNet50 was proposed in the study to obtain high-level feature representations for brain tumor diagnosis. The model offers a significant improvement in the diagnosis process by reducing misdiagnosis rates to 98.86% and 99.50% accuracy. The proposed methods make a significant contribution to enhancing diagnostic accuracy in clinical decision support systems, as they enable the efficient processing of complex morphological structures.
- BildiriYalnız künye2025Hybrid CNN-LSTM Framework for Automated Detection of Pediatric Heart Murmurs
Al-Taib, Asmaa Abduh; Ture, Hayati; Aslan, Muhammed Emin
This study presents a comprehensive deep learning framework for automated heart murmur detection from phonocardiogram (PCG) signals to address the subjective limitations of cardiac auscultation. Using the CirCor DigiScope dataset of 5,272 pediatric heart sound recordings from 1,568 subjects, we systematically evaluated multiple approaches including traditional machine learning models (Logistic Regression, SVM, KNN, Decision Tree, AdaBoost), basic deep learning architectures (standalone CNN and LSTM), and advanced transfer learning models (DenseNet121, EfficientNet B0/B1/B2, ResNet 50/101, MobileNet V2/V3, GhostNet, AttentionCNN). Our hybrid CNN-LSTM architecture combines mel-spectrogram spatial feature extraction with temporal sequence modeling using 128 traditional audio features and comprehensive preprocessing with data augmentation. The CNN-LSTM model achieved superior performance with 84.6% accuracy, 0.84 F1-score, 0.748 precision, and 0.800 recall, significantly outperforming traditional methods (best SVM: 0.716 F1-score) and advanced architectures (best DenseNet121: 0.72 F1-score), demonstrating robust classification with 0.879 AUC. Results indicate that hybrid deep learning architectures effectively automate cardiac auscultation, providing objective diagnostic tools with potential for improved healthcare accessibility and clinical decision-making.