Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2026/r/gumushane/14203
  Optic disc segmentation: Improved U-Net model and hyperparameter optimization with harmony search algorithm

  İncir, Ramazan; Bozkurt, Ferhat

  Vision loss negatively impacts individuals quality of life, creating a societal issue. As stated by the World Health Organization, vision loss is significant concern among health problems. Glaucoma and Diabetic Retinopathy are among the primary causes of vision impairment and loss. Early diagnosis of these diseases is crucial for guiding appropriate treatment. Optical disc segmentation plays a key role in diagnosing these diseases and helps in effectively detecting them through the analysis of retinal images. This study proposes method based on the automatic processing of fundus images to contribute to the early diagnosis of eye diseases. Improved U-Net architecture developed for optical disc segmentation is used. To enhance the models performance, the improved U-Net architecture has been strengthened with residual and custom convolution blocks. Genetic Algorithm, Particle Swarm Optimization Algorithm, Ant Colony Optimization and Harmony Search Algorithm were used for hyperparameter optimization. Comparisons showed that the Harmony Search Algorithm achieved better results, improving overall model performance and providing more accurate segmentation outcomes. The models performance has been tested on the IDRiD and DRISHTI-GS datasets. The improved architecture outperformed existing methods in the literature. Significant improvements were observed in key segmentation metrics such as IoU and Dice coefficient. In experimental studies using the IDRiD and DRISHTI-GS datasets, the proposed model achieved IoU scores of 93.65% and 95.20% and Dice scores of 96.72% and 97.54% respectively. The model was able to accurately define optical disc boundaries even in low contrast and complex images. The obtained success demonstrates the models potential as valuable tool for the early diagnosis of vision loss causing diseases.

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  İncele
• MakaleAçık Erişim2024/r/gumushane/14098
  Improving 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.

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