Sürdürülebilir Kalkınma Amaçları
Yazarlar
Yayınlar
- MakaleYalnız künye2026Investigating class granularity in historical aerial image segmentation: a comparative analysis of CNN and transformer-based models
Dihkan, Mustafa; Akbulut, Zeynep; Güler, Gülsena; Özdemir, Samed; Karsli, Fevzi
Historical aerial imagery acts as a crucial bridge to the pre-satellite era, becoming increasingly vital for quantifying long-term land cover changes and supporting retrospective environmental studies. This study presents a comprehensive comparison of CNN-based (DeepLabV3+, PSPNet, HRNet, ConvNeXt) and transformer-based (ViT, Swin Transformer, BEiT) architectures for semantic segmentation of historical aerial imagery. In particular, the potential of Transformer-based architectures for semantic segmentation of panchromatic historical aerial imagery in the context of increasing segmentation complexity remains underexplored. We assess how effectively each architecture handles increasing class granularity and captures finer-grained land-cover semantics. Experiments are conducted at three levels of detail: binary, three-class, and five-class segmentation. The dataset comprises panchromatic images from 1960, captured in Turkey. Precision, recall, F1-score, and intersection-over-union (IoU) metrics were employed to evaluate class-wise performance. In the binary ground and non-ground segmentation task, all architectures achieved mean F1-scores of 0.945–0.959 and mean IoU values of 0.905–0.921. In the three-class and five-class tasks, mean F1-scores ranged from 0.861–0.902 and 0.754–0.803, with mean IoU values of 0.777–0.831 and 0.623–0.682, respectively. In the three-class setting, ConvNeXt, Swin Transformer, and BEiT clustered towards the upper end of the mean metrics with only small differences among them. Increasing class granularity from three to five reduced mean performance across architectures, with decreases of up to 0.123 in mean F1-score and up to 0.176 in mean IoU. Across all classification levels, ConvNeXt consistently achieved higher performance, distinguishing itself specifically in the five-class task with the highest F1-scores and IoU values. ConvNeXt outperformed both the CNN-based baselines and Transformer-based models in the challenging building and road categories.
- MakaleYalnız künye2026A Comprehensive Deep-Learning Framework Integrating Lesion Segmentation and Stage Classification for Enhanced Diabetic Retinopathy Diagnosis
İncir, Ramazan; Bozkurt, Ferhat
Diabetic retinopathy (DR), one of the most prevalent microvascular complications of diabetes, stands as a leading cause of vision loss globally. Due to its asymptomatic nature in early stages, delayed diagnosis and staging may result in irreversible visual impairment. Therefore, accurate and simultaneous lesion segmentation and stage classification of DR are of critical clinical importance. In this study, a two-stage, end-to-end, holistic framework is proposed for automated DR diagnosis. In the first stage, an Improved U-Net architecture enhanced with residual blocks and additional convolutional layers is employed to segment small and low-contrast lesions such as microaneurysms, hemorrhages, and hard/soft exudates with high precision. Model hyperparameters are optimized using the harmony search algorithm to enhance training efficiency. In the second stage, lesion-based weight maps obtained from the segmentation step are applied to fundus images from the APTOS dataset, generating enriched inputs for classification. A vision transformer (ViT)-based model, augmented with a Convolutional Block Attention Module (CBAM), is utilized to improve feature extraction. In addition, features derived from ViT are further refined using a graph convolutional network (GCN) and traditional machine-learning classifiers. The proposed approach achieves high performance in multi-class DR stage classification. Compared to existing studies, the framework demonstrates notable improvements in both segmentation and classification accuracy, offering a robust and generalizable solution for DR diagnosis.
- MakaleAçık Erişim2025DSCIMABNet: A novel multi-head attention depthwise separable CNN model for skin cancer detection
Reis, Hatice Catal; Turk, Veysel
Skin cancer is a common type of cancer worldwide. Early diagnosis of skin cancer can reduce the risk of death by increasing treatment success. However, it is challenging for dermatologists or specialists because the symptoms are vague in the early stages and cannot be noticed by the naked eye. This study examines digital diagnostic techniques supported by artificial intelligence, focusing on early skin cancer detection and two methods have been proposed. In the first method, DSCIMABNet deep learning architecture was developed by combining multi-head attention and depthwise separable convolution techniques. This model provides flexibility in learning the dataset's local features, abstract concepts, and long-term relationships. The DSCIMABNet model and modern deep learning models trained on ImageNet are proposed to be combined with the ensemble learning method in the second method. This approach provides a comprehensive feature extraction process that will increase the performance of the classification process with ensemble learning. The proposed approaches are trained and evaluated on the ISIC 2018 dataset with image enhancement applied in preprocessing. In the experimental results, DSCIMABNet achieved 84.28% accuracy, while the proposed hybrid method achieved 99.40% accuracy. Moreover, on the Mendeley dataset (CNN for Melanoma Detection Data), DSCIMABNet achieved 92.58% accuracy, while the hybrid method achieved 99.37% accuracy. This study may significantly contribute to developing new and effective methods for the early diagnosis and treatment of skin cancer.
- MakaleAçık Erişim2024Fusion of transformer attention and CNN features for skin cancer detection
Reis, Hatice Catal; Turk, Veysel
Melanoma is a skin cancer that spreads quickly and has serious risks. Early diagnosis is essential, but since the symptoms of skin lesions in the early stages are vague and similar, they can be difficult for specialists to detect. Therefore, machine learning-based alternative diagnostic methods can be developed in addition to existing ones. This study proposes a new deep learning model, a modified lightweight vision transformer (ViT) architecture, and a hybrid framework developed with an integrated deep learning model and an Ensemble Learning (EL) model for the early-stage diagnosis of skin lesions. The proposed deep learning model was developed based on convolution layers and transformers. The model is called multi-head attention block depthwise separable convolution network (MABSCNET). The proposed hybrid framework was developed by combining modern deep learning and EL models pre-trained with the ImageNet dataset along with the MABSCNET model. In the experimental process, the effectiveness of the proposed methods was evaluated on the ISIC 2020 dataset. Additionally, additional experiments were conducted on ISIC 2018 and a Kaggle dataset to analyze the proposed hybrid framework's classification performance. Image enhancement techniques were used in the datasets. In the ISIC 2020 dataset, the MABSCNET model reached 78.63 % accuracy, the ViT model obtained 76.50 %, and the hybrid framework reached 92.74 % accuracy. Moreover, the proposed hybrid framework achieved 100 % on the ISIC 2018 dataset and 94.24 % on the Kaggle dataset.
- MakaleYayıncıda açık erişim2024Potato leaf disease detection with a novel deep learning model based on depthwise separable convolution and transformer networks
Catal Reis, Hatice; Turk, Veysel
Early diagnosis of plant diseases is essential in reducing economic losses for farmers and increasing production efficiency. Therefore, Computer-Aided Diagnosis (CAD) systems supported by artificial intelligence technologies can be developed to help diagnose diseases quickly and accurately by examining the symptoms and signs in plant leaves. In this study, Multi-head Attention Mechanism Depthwise Separable Convolution Inception Reduction Network (MDSCIRNet) architecture, an image-based deep convolutional neural network, is proposed for classifying potato leaf diseases. The main components of the MDSCIRNet architecture are depthwise separable convolution (DSC) and a multi-head attention mechanism. The proposed architecture has been compared with modern algorithms developed with DSC technology, such as Xception, MobileNet, and deep learning algorithms, such as ResNet101, InceptionV3, and EfficientNetB2, to evaluate its performance in the classification process. In addition, hybrid methods developed with the classical machine learning algorithms Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Adaptive Boosting (AdaBoost), and MDSCIRNet model, integrated deep learning model, hard voting ensemble learning model. Suggested methods such as these were also used in the experimental process. Moreover, techniques such as Contrast Limited Adaptive Histogram Equalization (CLAHE), Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN), and Hypercolumn were used to improve the image quality of the data set. In the experimental process, the MDSCIRNet deep learning architecture achieved 99.24% accuracy in the study using the original dataset. While a 99.11% accuracy rate was achieved with the integrated deep learning model and hard voting ensemble learning model, the highest success rate of 99.33% was performed in the study conducted with the MDSCIRNet + SVM method. This study contributes to developing new and effective strategies in the agricultural industry for the early diagnosis and control of potato plant diseases. Machine learning-based approaches offer the potential to minimize economic losses and increase productivity in production by allowing farmers to intervene early.