Sürdürülebilir Kalkınma Amaçları
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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.
- MakaleAçık Erişim2024Improving brain tumor classification with combined convolutional neural networks and transfer learning
İncir, Ramazan; Bozkurt, Ferhat
Brain tumors pose a serious threat, causing the deaths of thousands of people worldwide, and can lead to life-threatening consequences when not accurately diagnosed. The classification of brain tumors and the determination of correct treatment strategies are crucial, yet this process encounters various challenges. These tumors originate from different cell types and exhibit diversity in growth rates, histological features, and genetic structures. Some may show similarities at the microscopic level, complicating classification and making diagnosis and treatment challenging. The examination of brain MR images is a widely used method in diagnosing brain tumors. However, occasional misdiagnosis of tumors can lead to ineffective responses to treatments and reduced chances of survival for patients. Traditional machine learning classifiers require manually determined features, which is quite time-consuming. On the other hand, deep learning is highly effective in feature extraction and has recently been widely preferred in classification. In this context, the effectiveness of transfer learning architectures in brain tumor diagnosis was evaluated. Six different transfer learning architectures, including ResNet-50, MobileNet, VGG16, Inception-V3, DenseNet-121, and EfficientNetV2-M, were used in this study. A public MRI dataset was used for model validation and comparison with similar studies. To address the imbalance in the number of images among classes in the dataset, data augmentation techniques such as random rotation were applied during data preprocessing. Experiments revealed that the EfficientNetV2-M model outperformed other architectures with a 98.01% accuracy rate. Additionally, the study aimed to create a new model with more comprehensive feature extraction and generalization ability by combining the advantages of multiple models in different combinations. In this context, combinations of EfficientNetV2-M architecture with Inception-V3 and DenseNet-121 architectures were formed. Through these combinations, a concatenation-based EfficientNetV2-M + Inception-V3 model achieved a 98.41% accuracy value. It was observed that the proposed concatenation-based model outperformed advanced methods in improving medical imaging techniques and patient outcomes.