PubMed İndeksli Yayınlar Koleksiyonu
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Browsing PubMed İndeksli Yayınlar Koleksiyonu by Author "08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü"
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Article Citation - WoS: 6Citation - Scopus: 17Automatic Detection of Brain Tumors With the Aid of Ensemble Deep Learning Architectures and Class Activation Map Indicators by Employing Magnetic Resonance Images(Elsevier, 2024) Turk, Omer; Ozhan, Davut; Acar, Emrullah; Akinci, Tahir Cetin; Yilmaz, Musa; Türk, Ömer; Özhan, Davut; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 17.03. Department of Electronics and Automatization / Elektronik ve Otomasyon Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 17. Vocational Higher School / Meslek Yüksekokulu; 01. Mardin Artuklu University / Mardin Artuklu ÜniversitesiToday, as in every life-threatening disease, early diagnosis of brain tumors plays a life-saving role. The brain tumor is formed by the transformation of brain cells from their normal structures into abnormal cell structures. These formed abnormal cells begin to form in masses in the brain regions. Nowadays, many different techniques are employed to detect these tumor masses, and the most common of these techniques is Magnetic Resonance Imaging (MRI). In this study, it is aimed to automatically detect brain tumors with the help of ensemble deep learning architectures (ResNet50, VGG19, InceptionV3 and MobileNet) and Class Activation Maps (CAMs) indicators by employing MRI images. The proposed system was implemented in three stages. In the first stage, it was determined whether there was a tumor in the MR images Tumor) were detected from MR images (Multi-class Approach). In the last stage, CAMs of each tumor group were created as an alternative tool to facilitate the work of specialists in tumor detection. The results showed that the overall accuracy of the binary approach was calculated as 100% on the ResNet50, InceptionV3 and MobileNet architectures, and 99.71% on the VGG19 architecture. Moreover, the accuracy values of 96.45% with ResNet50, 93.40% with VGG19, 85.03% with InceptionV3 and 89.34% with MobileNet architectures were obtained in the multi-class approach.Article Citation - WoS: 3Citation - Scopus: 3Can deep learning replace histopathological examinations in the differential diagnosis of cervical lymphadenopathy?(Springer, 2024) Can, Sermin; Türk, Ömer; Ayral, Muhammed; Kozan, Günay; Arı, Hamza; Akdağ, Mehmet; Yıldırım Baylan, Müzeyyen; Türk, Ömer; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu ÜniversitesiIntroduction: We aimed to develop a diagnostic deep learning model using contrast-enhanced CT images and to investigate whether cervical lymphadenopathies can be diagnosed with these deep learning methods without radiologist interpretations and histopathological examinations. Material method: A total of 400 patients who underwent surgery for lymphadenopathy in the neck between 2010 and 2022 were retrospectively analyzed. They were examined in four groups of 100 patients: the granulomatous diseases group, the lymphoma group, the squamous cell tumor group, and the reactive hyperplasia group. The diagnoses of the patients were confirmed histopathologically. Two CT images from all the patients in each group were used in the study. The CT images were classified using ResNet50, NASNetMobile, and DenseNet121 architecture input. Results: The classification accuracies obtained with ResNet50, DenseNet121, and NASNetMobile were 92.5%, 90.62, and 87.5, respectively. Conclusion: Deep learning is a useful diagnostic tool in diagnosing cervical lymphadenopathy. In the near future, many diseases could be diagnosed with deep learning models without radiologist interpretations and invasive examinations such as histopathological examinations. However, further studies with much larger case series are needed to develop accurate deep-learning models.Article Citation - WoS: 16Citation - Scopus: 17A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data(Sage Journals, 2022) Uyulan, Caglar; Erguzel, Turker Tekin; Türk, Ömer; Farhad, Shams; Metin, Bariş; Tarhan, Nevzat; Türk, Ömer; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu ÜniversitesiAutomatic detection of Attention Deficit Hyperactivity Disorder (ADHD) based on the functional Magnetic Resonance Imaging (fMRI) through Deep Learning (DL) is becoming a quite useful methodology due to the curse of-dimensionality problem of the data is solved. Also, this method proposes an invasive and robust solution to the variances in data acquisition and class distribution imbalances. In this paper, a transfer learning approach, specifically ResNet-50 type pre-trained 2D-Convolutional Neural Network (CNN) was used to automatically classify ADHD and healthy children. The results demonstrated that ResNet-50 architecture with 10-k cross-validation (CV) achieves an overall classification accuracy of 93.45%. The interpretation of the results was done via the Class Activation Map (CAM) analysis which showed that children with ADHD differed from controls in a wide range of brain areas including frontal, parietal and temporal lobes.Article Comparison of Machine Learning Algorithms for Automatic Prediction of Alzheimer Disease(Lippincott Williams & Wilkins, 2025) Aslan, Emrah; Ozupak, Yildirim; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu ÜniversitesiBackground:Alzheimer disease is a progressive neurological disorder marked by irreversible memory loss and cognitive decline. Traditional diagnostic tools, such as intracranial volume assessments, electroencephalography (EEG) signals, and brain magnetic resonance imaging (MRI), have shown utility in detecting the disease. However, artificial intelligence (AI) offers promise for automating this process, potentially enhancing diagnostic accuracy and accessibility.Methods:In this study, various machine learning models were used to detect Alzheimer disease, including K-nearest neighbor regression, support vector machines (SVM), AdaBoost regression, and logistic regression. A neural network was constructed and validated using data from 150 participants in the University of Washington's Alzheimer's Disease Research Center (Open Access Imaging Studies Series [OASIS] dataset). Cross-validation was also performed on the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset to assess the robustness of the models.Results:Among the models tested, K-nearest neighbor regression achieved the highest accuracy, reaching 97.33%. The cross-validation on the ADNI dataset further confirmed the effectiveness of the models, demonstrating satisfactory results in screening and diagnosing Alzheimer disease in a community-based sample.Conclusion:The findings indicate that AI-based models, particularly K-nearest neighbor regression, provide promising accuracy for the early detection of Alzheimer disease. This approach has potential for further development into practical diagnostic tools that could be applied in clinical and community settings.Article Citation - WoS: 11Citation - Scopus: 14The Deep Learning Method Differentiates Patients With Bipolar Disorder From Controls With High Accuracy Using Eeg Data(Sage Publications inc, 2024) Metin, Baris; Uyulan, Caglar; Erguzel, Turker Tekin; Farhad, Shams; Cifci, Elvan; Turk, Omer; Tarhan, Nevzat; Türk, Ömer; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu ÜniversitesiBackground: Bipolar disorder (BD) is a mental disorder characterized by depressive and manic or hypomanic episodes. The complexity in the diagnosis of Bipolar disorder (BD) due to its overlapping symptoms with other mood disorders prompted researchers and clinicians to seek new and advanced techniques for the precise detection of Bipolar disorder (BD). One of these methods is the use of advanced machine learning algorithms such as deep learning (DL). However, no study of BD has previously adopted DL techniques using EEG signals. Method: EEG signals of 169 BD patients and 45 controls were cleaned from the artifacts and processed using two different DL methods: a one-dimensional convolutional neural network (1D-CNN) combined with the long-short term memory (LSTM) and a two-dimensional convolutional neural network (2D-CNN). Additionally, Class Activation Maps (CAMs) acquired from the bipolar and control groups were used to obtain distinctive regions to specify a particular class in an image. Results: Group identifications were confirmed with 95.91% overall accuracy through the 2D-CNN method, demonstrating very high sensitivity and lower specificity. Also, the overall accuracy obtained from the 1D-CNN + LSTM method was 93%. We also found that F4, C3, F7, and F8 electrode activities produce predominant features to detect the bipolar group. Conclusion: To our knowledge, this study used EEG-based DL analysis for the first time in BD. Our results suggest that the raw EEG-based DL algorithm can successfully differentiate individuals with BD from controls. Class Activation Map (CAM) analysis suggests that prefrontal changes are predominant in EEG data of patients with BD.Article Citation - WoS: 4Citation - Scopus: 3Deep Learning-Based Artificial Intelligence Can Differentiate Treatment-Resistant and Responsive Depression Cases With High Accuracy(Sage Publications inc, 2025) Metin, Sinem Zeynep; Uyulan, Caglar; Farhad, Shams; Erguzel, Tuerker Tekin; Turk, Omer; Metin, Baris; Tarhan, Nevzat; Türk, Ömer; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu ÜniversitesiBackground: Although there are many treatment options available for depression, a large portion of patients with depression are diagnosed with treatment-resistant depression (TRD), which is characterized by an inadequate response to antidepressant treatment. Identifying the TRD population is crucial in terms of saving time and resources in depression treatment. Recently several studies employed various methods on EEG datasets for automatic depression detection or treatment outcome prediction. However, no previous study has used the deep learning (DL) approach and EEG signals for detecting treatment resistance. Method: 77 patients with TRD, 43 patients with non-TRD, and 40 healthy controls were compared using GoogleNet convolutional neural network and DL on EEG data. Additionally, Class Activation Maps (CAMs) acquired from the TRD and non-TRD groups were used to obtain distinctive regions for classification. Results: GoogleNet classified the healthy controls and non-TRD group with 88.43%, the healthy controls and TRD subjects with 89.73%, and the TRD and non-TRD group with 90.05% accuracy. The external validation accuracy for the TRD-non-TRD classification was 73.33%. Finally, the CAM analysis revealed that the TRD group contained dominant features in class detection of deep learning architecture in almost all electrodes. Limitations: Our study is limited by the moderate sample size of clinical groups and the retrospective nature of the study. Conclusion: These findings suggest that EEG-based deep learning can be used to classify treatment resistance in depression and may in the future prove to be a useful tool in psychiatry practice to identify patients who need more vigorous intervention.Article Enhancing Schizophrenia Diagnosis Through Multi-View Eeg Analysis: Integrating Raw Signals and Spectrograms in a Deep Learning Framework(Sage Publications inc, 2025) Zan, Hasan; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu ÜniversitesiObjective: Schizophrenia is a chronic mental disorder marked by symptoms such as hallucinations, delusions, and cognitive impairments, which profoundly affect individuals' lives. Early detection is crucial for improving treatment outcomes, but the diagnostic process remains complex due to the disorder's multifaceted nature. In recent years, EEG data have been increasingly investigated to detect neural patterns linked to schizophrenia. Methods: This study presents a deep learning framework that integrates both raw multi-channel EEG signals and their spectrograms. Our two-branch model processes these complementary data views to capture both temporal dynamics and frequency-specific features while employing depth-wise convolution to efficiently combine spatial dependencies across EEG channels. Results: The model was evaluated on two datasets, consisting of 84 and 28 subjects, achieving classification accuracies of 0.985 and 0.994, respectively. These results highlight the effectiveness of combining raw EEG signals with their time-frequency representations for precise and automated schizophrenia detection. Additionally, an ablation study assessed the contributions of different architectural components. Conclusions: The approach outperformed existing methods in the literature, underscoring the value of utilizing multi-view EEG data in schizophrenia detection. These promising results suggest that our framework could contribute to more effective diagnostic tools in clinical practice.Article Citation - WoS: 129Citation - Scopus: 171Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals(MDPI, 2019) Türk, Ömer; Özerdem, Mehmet Siraç; Türk, Ömer; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu ÜniversitesiThe studies implemented with Electroencephalogram (EEG) signals are progressing very rapidly and brain computer interfaces (BCI) and disease determinations are carried out at certain success rates thanks to new methods developed in this field. The effective use of these signals, especially in disease detection, is very important in terms of both time and cost. Currently, in general, EEG studies are used in addition to conventional methods as well as deep learning networks that have recently achieved great success. The most important reason for this is that in conventional methods, increasing classification accuracy is based on too many human efforts as EEG is being processed, obtaining the features is the most important step. This stage is based on both the time-consuming and the investigation of many feature methods. Therefore, there is a need for methods that do not require human effort in this area and can learn the features themselves. Based on that, two-dimensional (2D) frequency-time scalograms were obtained in this study by applying Continuous Wavelet Transform to EEG records containing five different classes. Convolutional Neural Network structure was used to learn the properties of these scalogram images and the classification performance of the structure was compared with the studies in the literature. In order to compare the performance of the proposed method, the data set of the University of Bonn was used. The data set consists of five EEG records containing healthy and epilepsy disease which are labeled as A, B, C, D, and E. In the study, A-E and B-E data sets were classified as 99.50%, A-D and B-D data sets were classified as 100% in binary classifications, A-D-E data sets were 99.00% in triple classification, A-C-D-E data sets were 90.50%, B-C-D-E data sets were 91.50% in quaternary classification, and A-B-C-D-E data sets were in the fifth class classification with an accuracy of 93.60%.Article Citation - WoS: 6Citation - Scopus: 5How advantageous is it to use computed tomography image-based artificial intelligence modelling in the differential diagnosis of chronic otitis media with and without cholesteatoma?(European Review for Medical and Pharmacological Sciences, 2023) Türk, Ömer; Temiz, Hakan; Department of Basic Medical Sciences / Temel Tıp Bilimleri Bölümü; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 10. Faculty of Medicine / Tıp Fakültesi; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu ÜniversitesiAbstract. – OBJECTIVE: Cholesteatoma (CHO) developing secondary to chronic otitis media (COM) can spread rapidly and cause important health problems such as hearing loss. Therefore, the presence of CHO should be diagnosed promptly with high accuracy and then treated surgically. The aim of this study was to investigate the effectiveness of artificial intelligence applications (AIA) in documenting the presence of CHO based on computed tomography (CT) images. PATIENTS AND METHODS: The study was performed on CT images of 100 CHO, 100 non-cholesteatoma (N-CHO) COM, and 100 control patients. Two AIA models including ResNet50 and MobileNetV2 were used for the classification of the images. RESULTS: Overall accuracy rate was 93.33% for the ResNet50 model and 86.67% for the MobilNetV2 model. Moreover, the diagnostic accuracy rates of these two models were 100% and 95% in the CHO group, 90% and 85% in the N-CHO group, and 90% and 80% in the control group, respectively. CONCLUSIONS: These results indicate that the use of AIA in the diagnosis of CHO will improve the diagnostic accuracy rates and will also help physicians in terms of reducing their workload and facilitating the selection of the correct treatment strategy.Article A Hybrid 2d Gaussian Filter and Deep Learning Approach With Visualization of Class Activation for Automatic Lung and Colon Cancer Diagnosis(Sage Publications inc, 2024) Turk, Omer; Acar, Emrullah; Irmak, Emrah; Yilmaz, Musa; Bakis, Enes; Türk, Ömer; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu ÜniversitesiCancer is a significant public health issue due to its high prevalence and lethality, particularly lung and colon cancers, which account for over a quarter of all cancer cases. This study aims to enhance the detection rate of lung and colon cancer by designing an automated diagnosis system. The system focuses on early detection through image pre-processing with a 2D Gaussian filter, while maintaining simplicity to minimize computational requirements and runtime. The study employs three Convolutional Neural Network (CNN) models-MobileNet, VGG16, and ResNet50-to diagnose five types of cancer: Colon Adenocarcinoma, Benign Colonic Tissue, Lung Adenocarcinoma, Benign Lung Tissue, and Lung Squamous Cell Carcinoma. A large dataset comprising 25 000 histopathological images is utilized. Additionally, the research addresses the need for safety levels in the model by using Class Activation Mapping (CAM) for explanatory purposes. Experimental results indicate that the proposed system achieves a high diagnostic accuracy of 99.38% for lung and colon cancers. This high performance underscores the effectiveness of the automated system in detecting these types of cancer. The findings from this study support the potential for early diagnosis of lung and colon cancers, which can facilitate timely therapeutic interventions and improve patient outcomes.Article Citation - WoS: 4Citation - Scopus: 4In Vitro Antitumor and Antioxidant Capacity as Well as Ameliorative Effects of Fermented Kefir on Cyclophosphamide-Induced Toxicity on Cardiac and Hepatic Tissues in Rats(Mdpi, 2024) Demir, Cemil; Irmak, Halit; Cengiz, Mustafa; Irmak, Halit; Cengiz, Betul Peker; Ayhanci, Adnan; Department of Medical Services and Techniques / Tıbbi Hizmetler ve Teknikleri Bölümü; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 21. Vocational School of Health Services / Sağlık Hizmetleri Meslek Yüksekokulu; 01. Mardin Artuklu University / Mardin Artuklu ÜniversitesiFermented prebiotic and probiotic products with kefir are very important to slow down and prevent the growth of tumors and to treat cancer by stimulating the immune response against tumor cells. Cyclophosphamide (CPx) is widely preferred in cancer treatment but its effectiveness in high doses is restricted because of its side effects. The aim of this study was to investigate the protective effects of kefir against CPx-induced heart and liver toxicity. In an experiment, 42 Wistar albino rats were divided into six treatment groups: the control (Group 1), the group receiving 150 mg/kg CPx (Group 2), the groups receiving 5 and 10 mg/kg kefir (Groups 3 and 4) and the groups receiving 5 and 10 mg/kg kefir + CPx (Group 5 and 6). Fermented kefirs obtained on different days by traditional methods were mixed and given by gavage for 12 days, while a single dose of CPx was administered intraperitoneally (i.p.) on the 12th day of the experiment. It was observed that alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), lactate dehydrogenase (LDH), creatinine kinase-MB (CK-MB), ischemia modified albumin (IMA) and Troponin I values, which indicate oxidative stress, increased in the CPx-administered group, and this level approached that of the control in the CPx + kefir groups. Likewise, as a result of the kefir, the rats' CPx-induced histopathological symptoms were reduced, and their heart and liver tissue were significantly improved. In conclusion, it was observed that kefir had a cytoprotective effect against CPx-induced oxidative stress, hepatotoxicity and cardiotoxicity, bringing their biochemical parameters closer to those of the control by suppressing oxidative stress and reducing tissue damage.Article Citation - WoS: 7Citation - Scopus: 7Multi-task learning for arousal and sleep stage detection using fully convolutional networks(IOP Publishing, 2023) Zan, Hasan; Yıldız, Abdulnasir; Zan, Hasan; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu ÜniversitesiObjective: Sleep is a critical physiological process that plays a vital role in maintaining physical and mental health. Accurate detection of arousals and sleep stages is essential for the diagnosis of sleep disorders, as frequent and excessive occurrences of arousals disrupt sleep stage patterns and lead to poor sleep quality, negatively impacting physical and mental health. Polysomnography is a traditional method for arousal and sleep stage detection that is time-consuming and prone to high variability among experts. Approach: In this paper, we propose a novel multi-task learning approach for arousal and sleep stage detection using fully convolutional neural networks. Our model, FullSleepNet, accepts a full-night single-channel EEG signal as input and produces segmentation masks for arousal and sleep stage labels. FullSleepNet comprises four modules: a convolutional module to extract local features, a recurrent module to capture long-range dependencies, an attention mechanism to focus on relevant parts of the input, and a segmentation module to output final predictions. Main results: By unifying the two interrelated tasks as segmentation problems and employing a multi-task learning approach, FullSleepNet achieves state-of-the-art performance for arousal detection with an area under the precision-recall curve of 0.70 on Sleep Heart Health Study and Multi-Ethnic Study of Atherosclerosis datasets. For sleep stage classification, FullSleepNet obtains comparable performance on both datasets, achieving an accuracy of 0.88 and an F1-score of 0.80 on the former and an accuracy of 0.83 and an F1-score of 0.76 on the latter. Significance: Our results demonstrate that FullSleepNet offers improved practicality, efficiency, and accuracy for the detection of arousal and classification of sleep stages using raw EEG signals as input.Article Citation - WoS: 2Citation - Scopus: 2The Protection Afforded by Kefir Against Cyclophosphamide Induced Testicular Toxicity in Rats by Oxidant Antioxidant and Histopathological Evaluations(Nature Portfolio, 2024) Demir, Cemil; Irmak, Halit; Cengiz, Mustafa; Irmak, Halit; Cengiz, Betul Peker; Ayhanci, Adnan; Department of Medical Services and Techniques / Tıbbi Hizmetler ve Teknikleri Bölümü; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 21. Vocational School of Health Services / Sağlık Hizmetleri Meslek Yüksekokulu; 01. Mardin Artuklu University / Mardin Artuklu ÜniversitesiCyclophosphamide (CTX) is the most commonly used effective alkylating drug in cancer treatment, but its use is restricted because its toxic side effect causes testicular toxicity. CTX disrupts the tissue redox and antioxidant balance and the resulting tissue damage causes oxidative stress. In our study based on this problem, kefir against CTX-induced oxidative stress and testicular toxicity were investigated. Rats were divided into 6 groups: control, 150 mg/kg CTX, 5 and 10 mg/kg kefir, 5 and 10 mg/kg kefir + 150 CTX. While the fermented kefirs were mixed and given to the rats for 12 days, CTX was given as a single dose on the 12th day of the experiment. Testis was scored according to spermatid density, giant cell formation, cells shed into tubules, maturation disorder, and atrophy. According to our biochemical findings, the high levels of total oxidant status (TOS), and the low levels of total antioxidant status (TAS) in the CTX group, which are oxidative stress markers, indicate the toxic effect of CTX, while the decrease in TOS levels and the increase in TAS levels in the kefir groups indicate the protective effect of kefir. In the CTX-administered group, tubules with impaired maturation and no spermatids were observed in the transverse section of the testicle, while in the kefir groups, the presence of near-normal tubule structures and tubule lumens despite CTX showed the protective effect of kefir. In our study, it was observed that kefir had a protective and curative effect on CTX-induced toxicity and oxidative stress and could be a strong protector.
