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Öğe Assessing Lung Injury Induced by Streptozotocin-induced Diabetes: A Deep Neural Network Analysis of Histopathological and Immunohistochemical Images(Bentham Science Publ Ltd, 2025) Senturk, Tugba; Bolat, Demet; Yay, Arzu Hanim; Baran, Munevver; Latifoglu, FatmaIntroduction Diabetes mellitus is an endocrine disorder characterized by metabolic abnormalities and chronic hyperglycemia, caused by insulin deficiency (Type I) or resistance (Type II). It affects various tissues differently, and its complications extend beyond classical targets, such as the kidneys and eyes, to lesser-studied organs, including the lungs. Understanding tissue-specific damage is crucial for effective disease management and the prevention of complications.Objective This study aims to evaluate the histopathological and immunohistochemical effects of diabetic lung fibrosis using a streptozotocin (STZ)-induced diabetes model. Additionally, it seeks to develop a high-performance image classification system based on deep neural networks to accurately classify tissue damage in diabetic models.Methods Lung tissue samples were collected from the STZ-induced diabetes model and analyzed through histopathological and immunohistochemical techniques. Image data were further processed using convolutional neural networks (CNNs), including pre-trained models, such as ResNet50, VGG16, and SqueezeNet. Classification was conducted in multiple color spaces (RGB, Grayscale, and HSV) and evaluated using performance metrics, including confusion matrix, precision, recall, F1 score, and accuracy.Results The use of color significantly enhanced image patch classification performance. Among the models tested, SqueezeNet in the RGB color space demonstrated the highest accuracy, achieving an F1 score of 93.49% +/- 0.04 and an accuracy of 93.77% +/- 0.04. These results indicated the efficacy of CNN-based classification in detecting lung damage associated with diabetes.Discussion and Conclusion Our findings confirmed that diabetes induces histopathological changes in lung tissue, contributing to fibrosis and potential pulmonary complications. Deep learning-based classification methods, particularly when utilizing color space variations and advanced preprocessing techniques, provide a powerful tool for analyzing diabetic tissue damage and may aid in the development of diagnostic support systems.Öğe Detection of amyotrophic lateral sclerosis disease by variational mode decomposition and convolution neural network methods from event-related potential signals(Tubitak Scientific & Technological Research Council Turkey, 2021) Latifoglu, Fatma; Orhanbulucu, Firat; Ileri, RamisAmyotrophic lateral sclerosis (ALS), also known as motor neuron disease, is a neurological disease that occurs as a result of damage to the nerves in the brain and restriction of muscle movements. Electroencephalography (EEG) is the most common method used in brain imaging to study neurological disorders. Diagnosis of neurological disorders such as ALS, Parkinson's, attention deficit hyperactivity disorder is important in biomedical studies. In recent years, deep learning (DL) models have been started to be applied in the literature for the diagnosis of these diseases. In this study, event-related potentials (ERPs) were obtained from EEG signals obtained as a result of visual stimuli from ALS patients and healthy controls. As a new method, variational mode decomposition (VMD) is applied to the produced ERP signals and the signals are decomposed into subbands. In addition, empirical mode decomposition (EMD), one of the popular decomposition methods in the literature, was also analyzed, and ERP signals were divided into subbands and compared with the VMD method. Subband signals were classified in two stages with the one-dimensional convolutional neural network (1D CNN) model, which is one of the DL techniques proposed in the study. Accuracy, sensitivity, specificity, and F1-Score measurements were obtained using 5-and 10-fold cross-validation to evaluate classifier performance. In the first stage of classification, only VMD and EMD subband signals were used and 92.95% classification accuracy was obtained by the VMD method. In the second stage, VMD, EMD subband signals, and original ERP signals were all classified together with the VMD+ERP model achieving the maximum classification accuracy rate of 90.42%. It is thought that the results of the study will contribute to the diagnosis of similar neurological disorders such as ALS, attention studies based on visual stimuli, and the development of brain-computer interface (BCI) systems using the method applied to the proposed ERP signals.Öğe Detection of amyotrophic lateral sclerosis disease from event-related potentials using variational mode decomposition method(Taylor & Francis Ltd, 2022) Orhanbulucu, Firat; Latifoglu, FatmaThis study, it was aimed to contribute to the literature on Amyotrophic lateral sclerosis (ALS) diagnosis and Brain-Computer Interface (BCI) technologies by analyzing the electroencephalography (EEG) signals obtained as a result of visual stimuli and attention from ALS patients and healthy controls. It was observed that the success rate significantly increased both in the occipital and central regions in all classifiers, especially in the entropy features. The most successful classification was obtained with the Naive Bayes (NB) classifier using the Morphological Features (MF) + Variational Mode Decomposition (VMD) -Entropy features at 88.89% in the occipital region and 94.44% in the central region.Öğe Early diagnosis of pancreatic cancer by machine learning methods using urine biomarker combinations(Tubitak Scientific & Technological Research Council Turkey, 2023) Acer, Irem; Bulucu, Firat Orhan; Icer, Semra; Latifoglu, FatmaThe most common type of pancreatic cancer is pancreatic ductal adenocarcinoma (PDAC), which accounts for the vast majority of pancreatic cancers. The five-year survival rate for PDAC due to late diagnosis is 9%. Early diagnosed PDAC patients survive longer than patients diagnosed at a more advanced stage. Biomarkers can play an essential role in the early detection of PDAC to assist the health professional. Machine learning and deep learning methods are used with biomarkers obtained in recent studies for diagnostic purposes. In order to increase the survival rates of PDAC patients, early diagnosis of the disease with a noninvasive test is a critical need. Our study offers a promising approach for the early detection of PDAC with noninvasive urinary biomarkers and carbohydrate antigen 19-9 (CA19-9). The Kaggle Urinary Biomarkers for Pancreatic Cancer (2020) open-access dataset consisting of 590 participants was used in this study. Seven machine learning classifiers (support vector machine (SVM), naive Bayes (NB), k-nearest neighbors (kNN), random forest (RF), light gradient boosting machine (LightGBM), AdaBoost, and gradient boosting classifier (GBC)) to detect PDAC disease classifier were used. Binary and multiple classification processes were carried out. Data was validated in our study using 5-10-fold crossvalidation. This study aimed to determine the best machine learning model by analyzing the performance of machine learning models in determining the classes of healthy controls, pancreatic disorders, and patients with PDAC. It is a remarkable finding that ensemble learning models were more successful in all our groups. The most successful classification method in classifying healthy controls and patients with PDAC was CV-10, while the GBC (92.99%) model was (AUC = 0.9761). The most successful classification method in classifying patients with pancreatic disorders and PDAC was CV-10, while the LightGBM (86.37%) model was (AUC = 0.9348). In the classification of healthy controls, pancreatic disorders, and patients with PDAC, the most successful classification method was CV-5, while the GBC (72.91%) model was (AUC = 0.8733).Öğe EEG signal analysis for the classification of Alzheimer's and frontotemporal dementia: a novel approach using artificial neural networks and cross-entropy techniques(Taylor & Francis Ltd, 2026) Latifoglu, Fatma; Orhanbulucu, Firat; Murugappan, Murugappan; Gurbuz, Suemeyye Nur; Cayir, Burcin; Avci, Fatma ZehraDementia, a neurological disorder, can cause cognitive decline due to damage to the brain. Our study aims to contribute to the development of computer-aided diagnosis (CAD) systems to aid in the early diagnosis of Alzheimer's disease (AD) and frontotemporal dementia (FTD) by examining Electroencephalogram (EEG) signals. EEG signals of 36 AD, 23 FTD and 29 healthy control (HC) participants were analyzed and entropy measurement approaches were used to analyze the connectivity between EEG channel pairs. The cross-permutation entropy (CPE) method and the cross conditional entropy (CCE) method were analyzed separately and the fused cross entropy (FCE) method was also tested by combining these techniques to determine the most appropriate method for feature extraction from EEG signals. The features obtained from these techniques were then evaluated in the classification phase using two separate machine learning algorithms. According to the performance evaluation criteria, the FCE and artificial neural network (ANN) model showed the most successful performance in the classification of all groups. In terms of area under the curve (AUC) and accuracy rates, 99.85% AUC and 98.46% accuracy were obtained in AD&HC groups, 99.71% AUC and 98.10% accuracy in FTD&HC groups and 99.39% AUC, 96.61% accuracy in AD&FTD groups. With the same model, an AUC rate of 97.14% and accuracy rate of 73.86% was obtained for the classification of the triple group (AD&FTD&HC). It has been observed that the results of this study show successful performance compared to the results of similar studies.Öğe High-performance classification of STEMI and NSTEMI by automatic feature selection from ECG signals: a triple approach(Springer London Ltd, 2025) Latifoglu, Fatma; Icer, Semra; Guven, Aysegul; Zhusupova, Aigul; Avsarogullari, Omer Levent; Kelesoglu, Saban; Kalay, NihatAcute Myocardial Infarction (AMI) remains a major health problem globally despite advances in diagnosis and treatment. Although electrocardiography (ECG) is a popular diagnostic tool, it can be difficult to interpret due to signal variability and pathology-related changes. This study proposes a triple approach to classify Healthy Controls (HC), ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI) by applying the triple method to 12-lead ECG signals. The proposed method includes automatic feature and ECG derivation selection using Particle Swarm Optimisation (PSO), Least Absolute Shrinkage and Selection Operator (LASSO) and Linear Regression (LR) and signal decomposition using Variational Mode Decomposition (VMD). Classification is performed using machine learning algorithms such as Artificial Neural Network (ANN), K-nearest neighbours (KNN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). The proposed method is evaluated using both the original clinical dataset and the PTB-XL database. As a result of the evaluation, high classification performance was achieved for both the clinical dataset (Accuracy: 100%) and the open-source PTB-XL dataset (Accuracy: 99.60%). The results obtained in this study demonstrate the potential for fast and reliable diagnosis. The proposed work contributes to addressing the challenge of distinguishing between STEMI and NSTEMI, which is crucial for the treatment of AMI patients.Öğe Histopathological Image Analysis Using Machine Learning to Evaluate Cisplatin and Exosome Effects on Ovarian Tissue in Cancer Patients(Mdpi, 2025) Senturk, Tugba; Latifoglu, Fatma; Altintop, Cigdem Guluzar; Yay, Arzu; Gonen, Zeynep Burcin; Onder, Gozde Ozge; Mat, Ozge CengizCisplatin, a widely used chemotherapeutic agent, is highly effective in treating various cancers, including ovarian and lung cancers, but it often causes ovarian tissue damage and impairs reproductive health. Exosomes derived from mesenchymal stem cells are believed to possess reparative effects on such damage, as suggested by previous studies. This study aims to evaluate the reparative effects of cisplatin and exosome treatments on ovarian tissue damage through the analysis of histopathological images and machine learning (ML)-based classification techniques. Five experimental groups were examined: Control, cisplatin-treated (Cis), exosome-treated (Exo), exosome-before-cisplatin (ExoCis), and cisplatin-before-exosome (CisExo). A set of 177 Local Binary Pattern (LBP) features were extracted from histopathological images, followed by feature selection using Lasso regression. Classification was performed using ML algorithms, including decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and Artificial Neural Network (ANN). The CisExo group exhibited the most homogeneous texture, suggesting effective tissue recovery, whereas the ExoCis group demonstrated greater heterogeneity, possibly indicating incomplete recovery. KNN and ANN classifiers achieved the highest accuracy, particularly in comparisons between the Control and CisExo groups, reaching an accuracy of 87%. The highest classification accuracy was observed for the Control vs. Cis groups (approximately 91%), reflecting distinct features, whereas the Control vs. Exo groups demonstrated lower accuracy (around 68%) due to feature similarity. Exosome treatments, particularly when administered post-cisplatin, significantly improve ovarian tissue recovery. This study highlights the potential of ML-based classification as a robust tool for evaluating therapeutic outcomes. Additionally, it underscores the promise of exosome therapy in mitigating chemotherapy-induced ovarian damage and preserving reproductive health. Further research is warranted to validate these findings and optimize treatment protocols.Öğe Machine learning-based migraine analysis using retinal vessel diameters from optical coherence tomography: an alternative approach(Springer-Verlag Italia Srl, 2025) Orhanbulucu, Firat; Unlu, Metin; Sevim, Duygu Gulmez; Gultekin, Murat; Latifoglu, FatmaObjectiveMigraine is a primary headache disorder characterised by attacks of headache that are usually unilateral and throbbing in nature, may be accompanied by neurological symptoms, and, due to its complex pathophysiology, can affect not only the central nervous system but also structures such as the retinal vascular system. In recent years, retinal imaging techniques have emerged as a promising method for studying neuro-ophthalmological diseases. In this study, we aimed to predict migraine by evaluating the measurements made from retinal images obtained with Optical Coherence Tomography (OCT).Materials and methodsIn the present study, 70 eyes of migraine patients and 38 eyes of healthy control group were examined. In cases where there was an imbalance between the classes, the data were balanced by applying the SMOTE method, which is widely preferred in studies. In addition to age and gender data, features such as retinal artery and vein diameters and choroidal thickness measurements were used as data. Pearson's Correlation Coefficient method was applied to calculate the linear relationship between the features.ResultsClassification results were evaluated with Area Under the Curve (AUC), Accuracy (Acc), Kappa statistic (KS), F1-score (F1), and Matthews Correlation Coefficient (MCC) parameters. The most successful result in the classification process between migraine and healthy control was obtained with the LightGBM algorithm with 93.28% AUC, 91.14% Acc, 86.67% F1, 0.74 KS, and 0.76 MCC rates.ConclusionThe presented research can be considered as a preliminary study. The results of the research on the application of machine learning algorithms showed an effective performance in migraine prediction from OCT data. Ensemble-based Boosting model classifiers were more successful than traditional machine learning classifiers.Öğe Migraine Analysis with Cross Entropy Based Connectivity Feature: Investigation of Sensory Stimulus Conditions(Ieee, 2025) Orhanbulucu, Firat; Latifoglu, FatmaMigraine is one of the most common neurological disorders. Despite its high prevalence, the lack of research on migraine compared to other neurological disorders is striking. Visual or auditory sensory stimuli are effective in triggering migraine attacks. The relationships between different brain regions depending on the stimuli can be analyzed with brain connectivity properties by creating a connectivity matrix. In this study, the effect of sensory stimuli in migraine patients is analyzed by entropy-based connectivity analysis and machine learning algorithms Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms. At the same time, the classification process was carried out with a healthy control group according to the stimulus conditions in order to contribute to the development of systems that can help diagnose migraine. Unlike previous studies, calculations were made between electrode pairs by applying permutation and conditional Cross Entropy (CE) techniques to Electroencephalography (EEG) signals and connectivity feature matrix or map showing the connection between electrodes was created. In the analysis of the stimulus effect in migraine patients, the most successful classification was obtained between resting and auditory stimulus conditions in the ANN algorithm (Accuracy: 83.68%). In the classification of migraine patients with healthy control group, the ANN algorithm and auditory stimulus condition gave the most successful accuracy rate (Accuracy: 85.71%). According to the analyses in this study, it was determined that the auditory stimulus condition may show significant differences in certain channels in certain regions of the brain of migraine patients compared to visual stimulus and resting conditions and healthy participants. The proposed approach (Fused CE+ANN) has shown successful performance in analyzing the stimulus effect and predicting migraine in migraine patients. Cross entropy techniques can help to discover brain connectivity features that may occur in the brain activity information that may occur especially under stimulus effect.Öğe A New Hybrid Approach Based on Time Frequency Images and Deep Learning Methods for Diagnosis of Migraine Disease and Investigation of Stimulus Effect(Mdpi, 2023) Orhanbulucu, Firat; Latifoglu, Fatma; Baydemir, RecepMigraine is a neurological disorder that is associated with severe headaches and seriously affects the lives of patients. Diagnosing Migraine Disease (MD) can be laborious and time-consuming for specialists. For this reason, systems that can assist specialists in the early diagnosis of MD are important. Although migraine is one of the most common neurological diseases, there are very few studies on the diagnosis of MD, especially electroencephalogram (EEG)-and deep learning (DL)-based studies. For this reason, in this study, a new system has been proposed for the early diagnosis of EEG- and DL-based MD. In the proposed study, EEG signals obtained from the resting state (R), visual stimulus (V), and auditory stimulus (A) from 18 migraine patients and 21 healthy control (HC) groups were used. By applying continuous wavelet transform (CWT) and short-time Fourier transform (STFT) methods to these EEG signals, scalogram-spectrogram images were obtained in the time-frequency (T-F) plane. Then, these images were applied as inputs in three different convolutional neural networks (CNN) architectures (AlexNet, ResNet50, SqueezeNet) that proposed deep convolutional neural network (DCNN) models and classification was performed. The results of the classification process were evaluated, taking into account accuracy (acc.), sensitivity (sens.), specificity (spec.), and performance criteria, and the performances of the preferred methods and models in this study were compared. In this way, the situation, method, and model that showed the most successful performance for the early diagnosis of MD were determined. Although the classification results are close to each other, the resting state, CWT method, and AlexNet classifier showed the most successful performance (Acc: 99.74%, Sens: 99.9%, Spec: 99.52%). We think that the results obtained in this study are promising for the early diagnosis of MD and can be of help to experts.Öğe A novel approach for Parkinson's disease detection using Vold-Kalman order filtering and machine learning algorithms(Springer London Ltd, 2024) Latifoglu, Fatma; Penekli, Sultan; Orhanbulucu, Firat; Chowdhury, Muhammad E. H.Parkinson's disease (PD) is the second most common neurological disorder caused by damage to dopaminergic neurons. Therefore, it is important to develop systems for early and automatic diagnosis of PD. For this purpose, a study that will contribute to the development of systems for the automatic diagnosis of PD is presented. The Electroencephalography (EEG) signals were decomposed into sub-bands using adaptive decomposition methods, such as empirical mode decomposition, variational mode decomposition, and Vold-Kalman order filtering (VKF). Various features were extracted from the sub-band decomposed signals, and the significant ones were determined by Chi-squared test. These important features were applied as input to support vector machine (SVM), fitch neural network (FNN), k-nearest neighbours (KNN), and decision trees (DT), machine learning (ML) models and classification was performed. We analysed the performance of ML models by obtaining accuracy, sensitivity, specificity, positive predictive value, negative predictive values, F1-score, false-positive rate, kappa statistics, and area under the curve. The classification process was performed for two cases: PD ON-HC and PD OFF-HC groups. The most successful method in this study was the VKF method, which was applied for the first time in this field with the approach specified for both cases. In both instances, the SVM algorithm was employed as the ML model, with classifier performance criterion values close to 100%. The results obtained in this study seem to be successful compared to the results of recent research on the diagnosis of PD.
