WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14627/6
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Article Gba-Net: A Gated Bottleneck and Attention-Driven Architecture for Robust Ischemic Stroke Segmentation across Ct and Dwi(Baku State Univ, Inst Applied Mathematics, 2026) Pacal, I.; Ganie, S.M.Automated ischemic stroke segmentation remains difficult because non-contrast Computed Tomography (CT) is low contrast and noisy, whereas Diffusion Weighted Imaging (DWI) shows heterogeneous lesions. Conventional U-Net models rely on local receptive fields and unselective skip fusion, limiting global context and noise control. We propose GBA-Net, a UNet variant that combines a high-capacity gated Convolutional Neural Network (CNN) bottleneck for long range dependencies with convolutional block attention modules that refine multiscale features before decoder fusion. The bottleneck helps interpret subtle CT hypo densities and link scattered infarcts in DWI, while attention suppresses CT noise and filters high intensity mimics, improving boundary delineation. We evaluated GBA-Net on ISLES 2024 and TEKNOFEST 2021 and compared it with nine baselines including UNet, UNet++, DeepLabV3+, and Seg-Former. GBA-Net achieved Dice 0.7376 and 0.7140 and the best average ASSD of 4.73 pixels on CT.Article DiSCNet: Directional Split Convolution for Compute-Efficient Brain Tumor Diagnosis(Elsevier Sci Ltd, 2026) Pacal, Ishak; Ganie, Shahid MohammadBrain tumor classification from magnetic resonance imaging (MRI) remains challenging because tumor appearance varies substantially across patients, while scanner- and protocol-related differences can alter image intensity distributions and weaken model generalization. This study aims to develop a compact yet highperforming deep learning framework that can classify heterogeneous brain MRI more reliably without relying on large-capacity backbones. To this end, we propose DiSCNet, a lightweight architecture built on an InceptionNeXt-inspired hierarchical design and centered on a novel Directional Split Convolution (DiSC) block. The proposed block diversifies receptive fields through complementary local and directional depthwise branches, while Global Response Normalization and Efficient Channel Attention are incorporated to improve feature stability and channel selectivity under acquisition variability. The model was evaluated on a unified benchmark constructed from five publicly available MRI repositories, comprising 17,888 images across four classes: glioma, meningioma, pituitary and non-tumor. Under a single training and evaluation protocol, DiSCNet was compared against 71 contemporary convolutional, transformer-based, and hybrid architectures. DiSCNet achieved the best overall performance, with 0.9922 accuracy, 0.9916 precision, 0.9930 recall, and 0.9923 F1-score, while using only 2.78 million parameters. Class-wise analysis further showed strong and balanced recognition across all diagnostic categories, and Grad-CAM visualizations indicated that the model predominantly focused on lesionrelevant regions. These findings demonstrate that a carefully designed lightweight architecture can outperform substantially larger models and provide an efficient, robust, and clinically relevant solution for four-class brain tumor MRI classification.Article Denta-Hybridonet: A Hybrid CNN-Transformer Architecture for Automated Detection of Developmental Dental Anomalies in Pediatric Panoramic Radiographs(Elsevier Sci Ltd, 2026) Eskibaglar, Busra Karaagac; Yavuz, Yelda Polat; Dogan, Gizem Karagoz; Algarni, Ali; Cakmak, Yigitcan; Pacal, IshakAccurate identification of developmental dental anomalies (DDAs) in children is clinically important; however, interpreting panoramic radiographs can still vary across readers because of mixed dentition, anatomical overlap, and variable image quality. This variability may delay recognition and complicate early interventional planning. In this study, we curated a pediatric panoramic dataset of 2,001 radiographs (ages 6-14 years) spanning five categories: Dilaceration, Ectopy, Hypodontia, Taurodontism, and Healthy. All images were independently labeled by three experienced pediatric dentists. To avoid patient-level leakage, the dataset was divided into training, validation, and held-out test sets using a patient-wise split. We propose Denta-HybridoNet, a hybrid convolution-transformer architecture designed to capture both fine-grained tooth morphology and broader, arch-wide contextual patterns. Its InceptionNeXt-gMLP block supports multi-scale local representation learning, which helps the model focus on subtle morphological cues, whereas the Swin-gMLP block provides efficient global context modeling across the dental arch. In addition, a gated multilayer perceptron (gMLP) module refines the feature transformation through context-dependent modulation, strengthening diagnostically relevant signals while reducing the influence of irrelevant variation and radiographic noise. To ensure a fair comparison, we benchmarked Denta-HybridoNet against 22 recent convolutional and transformer-based models under the same training protocol and evaluation conditions. On the held-out test set, the proposed method achieved 91.15% accuracy and 91.20% F1 score, representing the best overall performance among the compared architectures. Ablation studies quantified the contributions of hybrid design and gMLP, and Grad-CAM analyses supported interpretability by highlighting clinically meaningful regions.Article Citation - WoS: 11Citation - Scopus: 23Improving Yolo Detection Performance of Autonomous Vehicles in Adverse Weather Conditions Using Metaheuristic Algorithms(Mdpi, 2024) Ozcan, Ibrahim; Altun, Yusuf; Parlak, CevahirDespite the rapid advances in deep learning (DL) for object detection, existing techniques still face several challenges. In particular, object detection in adverse weather conditions (AWCs) requires complex and computationally costly models to achieve high accuracy rates. Furthermore, the generalization capabilities of these methods struggle to show consistent performance under different conditions. This work focuses on improving object detection using You Only Look Once (YOLO) versions 5, 7, and 9 in AWCs for autonomous vehicles. Although the default values of the hyperparameters are successful for images without AWCs, there is a need to find the optimum values of the hyperparameters in AWCs. Given the many numbers and wide range of hyperparameters, determining them through trial and error is particularly challenging. In this study, the Gray Wolf Optimizer (GWO), Artificial Rabbit Optimizer (ARO), and Chimpanzee Leader Selection Optimization (CLEO) are independently applied to optimize the hyperparameters of YOLOv5, YOLOv7, and YOLOv9. The results show that the preferred method significantly improves the algorithms' performances for object detection. The overall performance of the YOLO models on the object detection for AWC task increased by 6.146%, by 6.277% for YOLOv7 + CLEO, and by 6.764% for YOLOv9 + GWO.