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Öğe Automatic detection of brain tumors with the aid of ensemble deep learning architectures and class activation map indicators by employing magnetic resonance images(ScienceDirect, 2022) Türk, Ömer; Ozhan, Davut; Acar, Emrullah; Akinci, Tahir Cetin; Yilmaz, MusaToday, 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 (Binary Approach). In the second stage, different tumor types (Normal, Glioma Tumor, Meningioma Tumor, Pituitary 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.Öğe Can 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, MüzeyyenIntroduction: 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.Öğe A 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, NevzatAutomatic 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.Öğe Classification of electroencephalogram records related to cursor movements with a hybrid method based on deep learning(Wiley Online Library, 2021) Türk, ÖmerIn brain computer interface (BCI), many transformation methods are used whenprocessing electroencephalogram (EEG) signals. Thus, the EEG can be represen-ted in different domains. However, designing an EEG-based BCI system withoutany transformation technique is a challenge. For this purpose, in this study, aBCI model is proposed without any transformation. The classification of cursordown and cursor up movements using the EEG signals received from the brain isaimed at in the proposed model. The EEG patterns were classified using twomethods. Firstly, EEG signals were classified by classic convolutional neural net-work (CNN). Secondly, proposed hybrid structure obtained the EEG features,which were classified by k-NN and SVM, using CNN. Classification with CNNarchitecture gave a result of 68.15% while the hybrid method using k-NN andSVM classifiers yielded 97.55% and 97.61% respectively. The hybrid proposedmethod were more successful than the studies in the literature.Öğe Classification of Epilepsy Types from Electroencephalogram Time Series Using Continuous Wavelet Transform Scalogram-Based Convolutional Neural Network(ASTM International, 2020) Türk, Ömer; Akpolat, Veysi; Varol, Sefer; Aluçlu, Mehmet Ufuk; Özerdem, Mehmet SiraçDuring the supervisory activities of the brain, the electrical activities of nerve cell clusters produce oscillations. These complex biopotential oscillations are called electroencephalogram (EEG) signals. Certain diseases, such as epilepsy, can be detected by measuring these signals. Epilepsy is a disease that manifests itself as seizures. These seizures manifest themselves in different characteristics. These different characteristics divide epilepsy seizure types into two main groups: generalized and partial epilepsy. This study aimed to classify different types of epilepsy from EEG signals. For this purpose, a scalogram-based, deep learning approach has been developed. The utilized classification process had the following main steps: the scalogram images were obtained by using the continuous wavelet transform (CWT) method. So, a one-dimension EEG time series was converted to a two-dimensional time-frequency data set in order to extract more features. Then, the increased dimension data set (CWT scalogram images) was applied to the convolutional neural network (CNN) as input patterns for classifying the images. The EEG signals were taken from Dicle University, Neurology Clinic of Medical School. This data consisted of four classes: healthy brain waves, generalized preseizure, generalized seizure, and partial epilepsy brain waves. With the proposed method, the average accuracy performance of three of the EEG records' classes (healthy, generalized preseizure, and generalized seizure), and that of all four classes of EEG records were 90.16 % (± 0.20) and 84.66 % (± 0.48). According to these results, regarding the specific accuracy ratings of the recordings, the healthy EEG records scored 91.29 %, generalized epileptic seizure records were at 96.50 %, partial seizure EEG records scored 89.63 %, and the preseizure EEG records had a 90.44 % rating. The results of the proposed method were compared to the results of both similar studies and conventional methods. As a result, the performance of the proposed method was found to be acceptable.Öğe The convolutional neural network approach from electroencephalogram signals in emotional detection(Concurrency Computation, 2021) Türk, Ömer; Özerdem, Mehmet SiraçAlthough brain-computer interfaces (BCI) progress rapidly, the desired success has not been achieved yet. One of these BCI is to detect emotional states in humans. An emotional state is a brain activity consisting of hormonal and mental reasons in the face of events. Emotions can be detected by electroencephalogram (EEG) signals due to these activities. Being able to detect the emotional state from EEG signals is important in terms of both time and cost. In this study, a method is proposed for the detection of the emotional state by using EEG signals. In the proposed method, we aim to classify EEG signals without any transform (Fourier transform, wavelet transform, etc.) or feature extraction method as a pre-processing. For this purpose, convolutional neural networks (CNNs) are used as classifiers, together with SEED EEG dataset containing three different emotional (positive, negative, and neutral) states. The records used in the study were taken from 15 participants in three sessions. In the proposed method, raw channel-time EEG recordings are converted into 28 × 28 size pattern segments without pre-processing. The obtained patterns are then classified in the CNN. As a result of the classification, three emotion performance averages of all participants are found to be 88.84%. Based on the participants, the highest classification performance is 93.91%, while the lowest classification performance is 77.70%. Also, the average f-score is found to be 0.88 for positive emotion, 0.87 for negative emotion, and 0.89 for neutral emotion. Likewise, the average kappa value is 0.82 for positive emotion, 0.81 for negative emotion, and 0.83 for neutral emotion. The results of the method proposed in the study are compared with the results of similar studies in the literature. We conclude that the proposed method has an acceptable level of performance.Öğe Diagnosis of schizophrenia based on transformation from EEG sub-bands to the image with deep learning architecture(Springer Science and Business Media Deutschland GmbH, 2023) Türk, Ömer; Aldemir, Erdoğan; Acar, Emrullah; Ertuğrul, Ömer FarukElectroencephalogram is a low-cost, non-invasive, and high-entropy signal and thus has huge potential for clinical diagnosis of neurological diseases and brain–computer interface applications. Schizophrenia is one of the most severe diseases that show behavioral manifestations that are easily uncovered by specialists. In this context, the electroencephalogram analysis becomes more important for the automatic diagnosis of schizophrenia disease in the clinical process. In this study, a deep learning architecture, namely ResNet, aims to classify schizophrenia is proposed. The proposed system transforms wavelet sub-bands of the electroencephalogram into two-dimensional image space, which is considered the main unique contribution of the study. Thus, the disease indicators and features included in images could be figured out. Moreover, a discussion on the class activation maps was made to give a wide perspective on the features related to the disease. The proposed system was implemented on a large-scale electroencephalogram database containing records from unhealthy and healthy patients in various phases. The ResNet was implemented in three modes to give a thorough perspective in terms of the metrics of the diagnosis accuracy. The proposed system achieves 92.94% diagnosis accuracy rate, and the result shows that the proposed transformation-based solution is owing to the features related to schizophrenia diseaseÖğe Employing deep learning architectures for image-based automatic cataract diagnosis(TÜBİTAK, 2021) Acar, Emrullah; Türk, Ömer; Ertuğrul, Ömer Faruk; Aldemir, ErdoğanVarious eye diseases affect the quality of human life severely and ultimately may result in complete vision loss. Ocular diseases manifest themselves through mostly visual indicators in the early or mature stages of the disease by showing abnormalities in optics disc, fovea, or other descriptive anatomical structures of the eye. Cataract is among the most harmful diseases that affects millions of people and the leading cause of public vision impairment. It shows major visual symptoms that can be employed for early detection before the hypermature stage. Automatic diagnosis systems intend to assist ophthalmological experts by mitigating the burden of manual clinical decisions and on health care utilization. In this study, a diagnosis system based on color fundus images are addressed for cataract disease. Deep learning-based models were performed for the automatic identification of cataract diseases. Two pretrained robust architectures, namely VGGNet and DenseNet, were employed to detect abnormalities in descriptive parts of the human eye. The proposed system is implemented on a wide and unique dataset that includes diverse color retinal fundus images that are acquired comparatively in low-cost and common modality, which is considered a major contribution of the study. The dataset show symptoms of cataracts in different phases and represents the characteristics of the cataract. By the proposed system, dysfunction associated with cataracts could be identified in the early stage. The achievement of the proposed system is compared to various traditional and up-to-date classification systems. The proposed system achieves 97.94% diagnosis rate for cataract disease grading.Öğe Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals(MDPI, 2019) Türk, Ömer; Özerdem, Mehmet SiraçThe 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%.Öğe Epileptik EEG Sinyallerinin Sınıflandırılması için Bir Boyutlu Medyan Yerel İkili Örüntü Temelli Öznitelik Çıkarımı(2017) Türk, Ömer; Özerdem, Mehmet SiraçElektroansefalogram (EEG), epilepsi tespitinde yaygın olarak kullanılan önemli bir veri kaynağıdır. Bu çalışmada da Bonn Üniversitesi Epileptoloji bölümü veritabanından alınan ve A, B, C, D, E olmak üzere 5 işaret grubundan oluşan EEG kayıtları kullanılmıştır. Bir boyutklu medyan yerel ikili örüntü (1B-MYİÖ) yöntemi uygulanarak elde edilen özniteliklerin k-En Yakın Komşu (k-NN) sınıflandırıcısı ile sınıflandırılması amaçlanmıştır. Çalışmada geliştirilen 1BMYİÖ yönteminin öznitelik olarak sınıflandırma başarısı değerlendirilmiştir. Bu sınıflandırma için karışıklık matrisi hesaplanarak model başarım ölçümü yapılmıştır. Çalışmada A-E veri setleri için sınıflandırma performansı %100, A-D veri setleri için %99.00, D-E veri setleri için %98.00, E-CD veri setleri için %99.50 ve A-D-E veri setleri için de %96.00 olarak bulunmuştur. Çalışmada kullanılan 1B-MYİÖ yönteminin, literatürde kullanılan birçok yöntemden daha iyi sonuç verdiği görülmüştür.Öğe FPGA simulation of chaotic tent map-based S-Box design(Wiley Online Library, 2022) Türk, ÖmerThe chaotic system has a characteristically random behavior by nature, and these systems have their own characteristics in a completely deterministic structure. This feature of a chaotic system makes it difficult to predict encryptions designed based on such a system. Thanks to this unpredictable and strong feature, maps produced from chaotic systems are an important alternative in the field of encryption. One of the structures obtained by employing chaotic maps is the substitution box. S-Box, which provides the confusion principle used in block ciphers, is the main block that dynamically replaces unencrypted data with confidential data and makes a significant contribution to ensuring high security in the encryption system. Therefore, S-Boxes hold a critical role in block ciphers. Speed and reliability are important parameters in the creation of this main block. Especially, applications performed on hardware are more reliable and high performance. Therefore, in this study, an S-Box was designed using fieldprogrammable gate arrays (FPGA) simulation from a chaotic tent map to create a fast and reliable S-Box because FPGAs offer solutions that may be important in this field considering their fast and customizable architecture. In the proposed method, the S-Box was created in 0.16 s. In addition, the dynamic properties of the chaotic tent map were analyzed with Lyapunov exponents, and the NIST SP 800-22 test was applied for the information encryption suitability of the proposed chaotic system. Also, to test the reliability of the produced S-Box structures, SAC, non-linearity, bit independence criteria, and input/output XOR distribution table metrics were implemented. The results showed that the proposed chaotic map was dynamic and passed the reliability tests successfully.Öğe Identification of cotton and corn plant areas by employing deep transformer encoder approach and different time series satellite images: A case study in Diyarbakir, Turkey(ScienceDirect, 2023) Türk, Ömer; Şimşek Bağcı, Reyhan; Acar, EmrullahIt is very important to determine the crops in the agricultural field in a short time and accurately. Thanks to the satellite images obtained from remote sensing sensors, information can be obtained on many subjects such as the detection and development of agricultural products and annual product forecasting. In this study, it is aimed to automatically detect agricultural crops (corn and cotton) by using Sentinel-1 and Landsat-8 satellite image indexes via a new deep learning approach (Deep Transformer Encoder). This work was carried out in several stages, respectively. In the first stage, a pilot area was determined to obtain Sentinel-1 and Landsat-8 satellite images of agricultural crops used in this study. In the second stage, the coordinates of 100 sample points from this pilot area were taken with the help of GPS and these coordinates were then transferred to Sentinel-1 and Landsat-8 satellite images. In the next step, reflection and backscattering values were obtained from the pixels of the satellite images corresponding to the sample points of these agricultural crops. While creating the data sets of satellite images, the months of June, July, August and September for the years 2016–2021, when the development and harvesting times of agricultural products are close to each other, were preferred. The image data set used in the study consists of a total of 434 images for Sentinel-1 satellite and a total of 693 images for Landsat-8. At the last stage, the datasets obtained from different satellite images were evaluated in three different categories for crop identification with the aid of Deep Transformer Encoder approach. These are: (1-) Crop identification with only Sentinel-1 dataset, (2-) Crop identification only with Landsat-8 dataset, (3-) Crop identification with both Sentinel-1 and Landsat-8 datasets. The results showed that 85%, 95% and 87.5% accuracy values were obtained from the band parameters of Sentinel-1 dataset, Landsat-8 dataset and Sentinel-1&Landsat-8 datasets, respectivelyÖğe Palmprint recognition system based on deep region of interest features with the aid of hybrid approach(SpringerLink, 2023) Türk, Ömer; Çalışkan, Abidin; Acar, Emrullah; Ergen, BurhanPalmprint recognition system is a biometric technology, which is promising to have a high precision. This system has started to attract the attention of researchers, especially with the emergence of deep learning techniques in recent years. In this study, a deep learning and machine learning-based hybrid approach has been recommended to recognize palmprint images automatically via region of interest (ROI) features. The proposed work consists of several stages, respectively. In the first stage, the raw images have been collected from the PolyU database and preprocessing operations have been implemented in order to determine ROI areas. In the second stage, deep ROI features have been extracted from the preprocessed images with the aid of deep learning technique. In the last stage, the obtained deep features have been classified by employing a hybrid deep convolutional neural network and support vector machine models. Finally, it has been observed that the overall accuracy of the proposed system has achieved very successful results as 99.72% via hybrid approach. Moreover, very low execution time has been observed for whole process of the proposed system with 0.10 s.
