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• MakaleYalnız künye2026/r/gumushane/13913
  Efficient ensemble learning with multi-scale fusion based deep neural network for skin lesion classification

  Reis, Hatice Catal; Turk, Veysel

  Melanoma, a type of skin cancer, is a severe skin cancer that is critical for early diagnosis but difficult to detect. Deep learning is promising in melanoma diagnosis because it can automatically analyze visual features and perform end-to-end learning. However, achieving the optimum balance between performance and computational efficiency for the effective use of these models in clinical applications is an important research topic. In this study, three innovative methods are proposed for skin cancer detection: PADSCNet, P-DEFF, and M–DEFF. The PADSCNet is an effective neural network developed with CNN and multi-head attention techniques, designed with fusion methods, and stands out with its minimum number of parameters despite being quite deep. The P-DEFF and M–DEFF are based on ensemble learning techniques; P-DEFF was developed with a single deep learning model, and M–DEFF was developed with multiple pre-trained deep learning models. The proposed approaches were tested on the Skin Cancer: Malignant vs. Benign dataset, which was pre-processed using image enhancement techniques. According to the experimental results, PADSCNet achieved accuracy rates of 88.12 %, P-DEFF 93.75 %, and M–DEFF 95.00 %. In additional experiments conducted with the PADSCNet model, 93.12 % accuracy rates were achieved in the CNN for Melanoma Detection Dataset, and 95.00 % accuracy rates were achieved in the PH2 dataset. These findings show that the proposed methods are effective in skin cancer detection and can achieve successful results in clinical applications.

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• MakaleYalnız künye2026/r/gumushane/13762
  CropHealthNet: Potato Disease Detection Using Depthwise Separable Convolutional Neural Networks

  Catal Reis, Hatice

  Plant diseases are one of the primary factors that threaten food security, crop yields, harvesting, and sustainable production in the agricultural sector. In this context, early disease diagnosis is essential for enhancing agricultural productivity and reducing crop losses. In modern agricultural practices, machine learning-based approaches offer effective and innovative solutions for detecting and classifying plant diseases. These modern and innovative methods generally reach high accuracy rates with large-scale image datasets. In particular, deep learning algorithms have achieved significant success in modeling disease’s spread dynamics and detecting early disease symptoms. In this study, a convolutional neural network (CNN)-based, lightly parallel deep learning model called CropHealthNet is proposed for classifying potato leaf diseases. The model is designed with special depthwise separable convolution-based convolution layers to reduce the computational cost of CNN. CropHealthNet is a low-parameter model suitable for real-time operation, capable of recognizing disease-specific patterns with high accuracy. CropHealthNet aims to achieve deep and comprehensive feature extraction through its parallel structure and varying channel numbers. The features from each parallel structure are combined at multiple levels, increasing the model’s ability to detect abstract and deep patterns. Moreover, the proposed model has extremely low computational complexity with only 0.47 million parameters and 0.59 GFLOPs, thus offering high efficiency compared to existing approaches. The effectiveness of the proposed model was evaluated using the PlantVillage Potato Dataset. Since the PlantVillage Potato Dataset has an unbalanced data distribution, the number of data for each class has been equalized using the Synthetic Minority Over-sampling Technique (SMOTE) to evaluate the model’s performance against data imbalance. The proposed model succeeded in achieving 99.30% and 99.50% accuracy rates in the tests conducted with unbalanced and balanced datasets, respectively. Experimental findings show that the proposed model generalizes well despite data imbalance and correctly classify diseases on potato leaves. Therefore, the model can demonstrate strong performance in agricultural practices and can detect plant diseases under real-world conditions.

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• MakaleYalnız künye2026/r/gumushane/13716
  Multi-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.

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• MakaleYalnız künye2026/r/gumushane/13501
  A multi-scale context-aware deep learning framework for medical disease diagnosis

  Reis, Hatice Catal; Turk, Veysel

  Early and accurate diagnosis of brain tumors, skin lesions, and diabetic retinopathy is essential for effective treatment planning and improving patient prognosis. Nevertheless, the diagnostic process for these conditions remains challenging due to the reliance on conventional methods, which are often subjective, time-intensive, and dependent on expert interpretation. In this context, deep learning-based approaches offer promising opportunities to enhance diagnostic precision and support clinical decision-making. This study introduces BSCRADNet, a novel deep learning model designed for the automated diagnosis of oncological and retinopathic diseases. The proposed architecture employs a multi-layered analytical framework that integrates local and spatial features with long-range contextual dependencies, enabling the effective recognition of complex morphological patterns in medical images. By enhancing diagnostic consistency, BSCRADNet reduces the risk of misclassification—particularly in cases characterized by high inter-observer variability or subtle lesion presentation. The model demonstrated strong performance across multiple datasets, achieving classification accuracies of 94.67% for brain tumors, 89.58% for skin cancer, and 90.40% for diabetic retinopathy. Furthermore, to improve the discriminative power of feature representations across diverse disease categories, a composite hybrid architecture combining BSCRADNet with ResMLP was developed. This hybrid model yielded competitive results, with accuracies of 93.33%, 88.19%, and 87.40% for the respective diagnostic tasks. Experimental findings indicate that the proposed approach is capable of distinguishing intricate patterns in medical imaging data and holds significant potential for integration into clinical diagnostic workflows, contributing to more reliable and efficient disease classification.

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• MakaleYalnız künye2025/r/gumushane/14331
  A deep neural network combined with a two-stage ensemble model for detecting cracks in concrete structures

  Reis, Hatice Catal; Turk, Veysel; Kaya Yildiz, Cagla Melisa; Bozkurt, Muhammet Furkan; Karagoz, Seray Nur; Ustuner, Mustafa

  Detection of cracks in concrete structures is critical for their safety and the sustainability of maintenance processes. Traditional inspection techniques are costly, time-consuming, and inefficient regarding human resources. Deep learning architectures have become more widespread in recent years by accelerating these processes and increasing their efficiency. Deep learning models (DLMs) stand out as an effective solution in crack detection due to their features such as end-to-end learning capability, model adaptation, and automatic learning processes. However, providing an optimal balance between model performance and computational efficiency of DLMs is a vital research topic. In this article, three different methods are proposed for detecting cracks in concrete structures. In the first method, a Separable Convolutional with Attention and Multi-layer Enhanced Fusion Network (SCAMEFNet) deep neural network, which has a deep architecture and can provide a balance between the depth of DLMs and model parameters, has been developed. This model was designed using a convolutional neural network, multi-head attention, and various fusion techniques. The second method proposes a modified vision transformer (ViT) model. A two-stage ensemble learning model, deep feature-based two-stage ensemble model (DFTSEM), is proposed in the third method. In this method, deep features and machine learning methods are used. The proposed approaches are evaluated using the Concrete Cracks Image Data set, which the authors collected and contains concrete cracks on building surfaces. The results show that the SCAMEFNet model achieved an accuracy rate of 98.83%, the ViT model 97.33%, and the DFTSEM model 99.00%. These findings show that the proposed techniques successfully detect surface cracks and deformations and can provide practical solutions to real-world problems. In addition, the developed methods can contribute as a tool for BIM platforms in smart cities for building health.

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• MakaleAçık Erişim2025/r/gumushane/14286
  Advanced brain tumor analysis: a novel strategy for segmentation and classification using modern computational methods

  Reis, Hatice Catal; Turk, Veysel

  Despite their low incidence, brain tumors are one of the most invasive cancer types, constituting a significant burden of death and disease in all age groups. Early and accurate diagnosis of brain tumors plays a vital role in reducing mortality rates. The heterogeneous nature of brain tumors and the diversity of tumor lesions may make it difficult for radiologists to make the right decision in the manual diagnosis process. This study proposes the use of machine learning methods for the classification of brain tumors (pituitary, meningioma, and glioma) and the use of metaheuristic algorithms graph theory, and random walker algorithms in the segmentation of brain tumors. The classification performed with the proposed method obtained an overall accuracy rate of 98.33%. In addition, the classification accuracy of 99.50%, 99.50%, 98.67%, and 99.00% was achieved for no tumor, pituitary, meningioma, and glioma, respectively. Experiments in the segmentation process show that metaheuristic algorithms and max-flow graph cut approach produce successful results.

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• MakaleAçık Erişim2025/r/gumushane/14116
  Post-seismic structural assessment: advanced crack detection through complex feature extraction using pre-trained deep learning and machine learning integration

  Reis, Hatice Catal; Turk, Veysel; Ustuner, Mustafa; Kaya Yildiz, Cagla Melisa; Tatli, Ramazan

  Earthquakes can often cause significant damage to buildings. After an earthquake, experts/managers need to make quick and accurate damage assessments. Traditionally, manual analysis processes have been widely used in damage assessment studies. The fact that these methods are time-consuming and based on human observation leads to certain limitations in damage assessment studies. In recent years, artificial intelligence techniques such as deep learning and machine learning have frequently been preferred in damage detection studies, and significant success has been achieved. This study aimed to automatically detect cracks/damages in the buildings in Diyarbakir city after the February 6, 2023 Kahramanmaras, Turkey earthquake. Our experimental dataset was collected by the researchers and named Kahramanmaras-Diyarbakir Earthquake Building Crack Dataset (KDBECD-2023). The data set consists of four categories in terms of damage level: undamaged, slightly damaged, moderately damaged, and heavily damaged buildings. DenseNet201 deep learning architecture and popular machine learning algorithms, Support Vector Machine, Random Forest, Decision Tree, and K-Nearest Neighbors (KNN) were used to classify cracks at different damage levels. In the experimental phase, feature extraction was performed with the DenseNet201 architecture. In addition, dimensional reduction was applied with the Principal Component Analysis method to reduce the computational complexity of the proposed hybrid study. According to the experimental results, the DenseNet201-KNN hybrid model gave the most successful result with an accuracy value of 94.62%. The results of this study can make important contributions to decision makers and experts in detecting cracks and damages in buildings after an earthquake.

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• MakaleAçık Erişim2025/r/gumushane/14027
  DSCIMABNet: 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.

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• MakaleAçık Erişim2025/r/gumushane/12403
  A multi-stage fusion deep learning framework merging local patterns with attention-driven contextual dependencies for cancer detection

  Catal Reis, Hatice; Turk, Veysel

  Cancer is a severe threat to public health. Early diagnosis of disease is critical, but the lack of experts in this field, the personal assessment process, the clinical workload, and the high level of similarity in disease classes make it difficult. In recent years, deep learning-based artificial intelligence models have shown promise, with the potential to increase diagnosis speed and accuracy. These models attract attention with their automatic learning and adaptation capabilities. In this study, the deep learning-based PADBSRNet model and the PADBSRNet-Vision Transformer (ViT) hybrid method are proposed for the detection of brain tumors and skin and lung cancers. PADBSRNet is a comprehensive deep neural network architecture that integrates separable and traditional convolution layers, multiple attention mechanisms, bidirectional recurrent neural networks, and cross-connections/multi-stage feature fusion strategies. This architecture offers significant advantages in terms of effectively extracting local-global, contextual features and accurately modeling long-term dependencies in image classification tasks. The second proposed approach developed a hybrid method that combines the advantages of the PADBSRNet model and the ViT model. Experimental analysis on medical datasets such as the Figshare Brain Tumor Dataset, IQ-OTH/NCCD Dataset, and Skin Cancer: Malignant vs. Benign Dataset has evaluated the proposed models' performances. According to the experimental results, the PADBSRNet model has shown successful performance with 95.24 %, 99.55 %, and 88.61 % accuracy rates, respectively. The experimental findings show that the proposed deep learning model can effectively learn the complex relationships and hidden patterns of cancer disease, thus producing applicable and effective results in cancer diagnosis.

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• MakaleAçık Erişim2024/r/gumushane/14384
  Integrated deep learning and ensemble learning model for deep feature-based wheat disease detection

  Catal Reis, Hatice; Turk, Veysel

  Early detection of plant diseases is critical to prevent disease spread and assist farmers. Thanks to their high discrimination ability, Convolutional Neural Network (CNN)-based architectures can offer practical solutions in identifying different plant diseases. This study proposes a combined method to classify wheat plant diseases using the Integrated Deep Learning Framework (IDLF) and ensemble learning (EL) model. The proposed method uses pre-trained deep neural networks (ImageNet -based model (w/pre-training)). In addition, in the classification process, the performance of hybrid methods consisting of 13 deep learning architectures (DLA) trained from scratch, pre-trained DLA, deep, and-machine learning models were analyzed. Moreover, the use of hypercolumn, contrast stretching, and “Contrast Limited Adaptive Histogram Equalization (CLAHE)” techniques are used to improve the image quality of the dataset. It is aimed to obtain a strong classification performance by combining these methods. In the experimental process, in the study using the original dataset, the RegNetY080 model trained from scratch had an accuracy value of 97.64%; in the study with the optimized dataset with CLAHE, an accuracy value of 98.33%; in the study performed with the CLAHE-improved dataset and transfer learning method, an accuracy value of 99.58% was obtained. An accuracy of 99.58% was achieved in the proposed hybrid study with the pre-trained RegNetY080 model and Random Forest (RF). The proposed method using IDLF and EL model provided 99.72% accuracy. When the proposed method was applied to the Wheat Leaf Dataset and Wheat Leaf Disease dataset, 97.56% accuracy was obtained for the Wheat Leaf Dataset and 99.43% for Wheat Leaf Disease. The results of this study can be helpful to experts and farmers in the detection of plant diseases.

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