2017 25th Signal Processing and Communications Applications Conference (SIU), 2017
In this study, an SVM-based system is proposed for the classification of facial expressions that ... more In this study, an SVM-based system is proposed for the classification of facial expressions that are represented in 3D. Distance based features are used as a feature vector, which are determined by the distances between the different key points on the image. Study was conducted on a subset (Happy, sadness, surprise) of Bosphorus 3D Face Database. 9 different fiducial points are used to calculate a total of 5 distance features. SVM classification was performed with K-fold cross validation thus mean classification performance of different training and test clusters were determined. %85 success rate has achieved as a result of the expression analysis performed on the 3D facial scans.
2020 28th Signal Processing and Communications Applications Conference (SIU), 2020
In recent years, deep learning methods have achieved high success as solution to problems in the ... more In recent years, deep learning methods have achieved high success as solution to problems in the computer vision. Especially, CNN algorithms that extract information from the image is widely applied in logo detection. In this case, the recognition of trademark in trademark applications or infringement has been one of the major problems in the literature in terms of companies. In this paper, the dataset containing the logos of banks acquired from public domain images was collected in order to perform logo recognition, by using Faster R-CNN, an approach for the recognition of the bank logo in the video have been developed and as a result average accuracy of %98 was obtained.
Son zamanlarda goruntu isleme ile ilgili gelismeler, hizla gelisen teknolojik sistemlerin ilerlem... more Son zamanlarda goruntu isleme ile ilgili gelismeler, hizla gelisen teknolojik sistemlerin ilerlemesinde katkida bulunmustur. Ozellikle saglik alanindaki goruntu isleme ile ilgili calismalar populerligini daha da artirmistir. Gerek tibbi goruntuler olsun gerekse diger alandaki goruntuler olsun, mevcut yontemler uzerinde basari saglatilmasina ragmen; derin ogrenme modeli, mevcut yontemlere kiyasla zaman ve performans acisindan daha fazla katkida bulunan bir modeldir. Mevcut yontemler ile tek katmanli goruntuler uzerinden islem yapiliyorken, derin ogrenme modeliyle, cok katmanli goruntuler uzerinden performansi yuksek sonuclar alinabilmektedir. Derin ogrenmenin en onemli ozelligi, goruntu uzerindeki islemleri tek bir sefer de isleme tabi tutan ve el ile girilmesi gereken parametreleri kendi kendine kesif edebilmesidir. Ayrica teknoloji firmalarinin da derin ogrenmeye yonelmesi, kendi aralarinda rekabet gucunu artirdigi gibi, bilimsel anlamda derin ogrenme uzerine kurduklari yontemler, ...
Zaturre Hastaligi, insanin hayatinin herhangi bir doneminde karsilasabilecegi hastaliklardan biri... more Zaturre Hastaligi, insanin hayatinin herhangi bir doneminde karsilasabilecegi hastaliklardan biridir. Enfeksiyon hastaliklarinin yaklasik %18’ini zaturre hastaligi olusturmaktadir. Bu hastalik ilerleyen bazi durumlarda olume sebep olabilmektedir. Tibbi olarak zaturre teshisini kesin olarak konulabilmesi icin akciger rontgen goruntulerinin bir doktor tarafindan incelenmesi gereklidir. Bu calismada, zaturre hastaliginin teshisi icin gelistirilen tanima sistemi icin erisime acik olan akciger rontgen goruntulerinden faydalanilmistir. Elde edilen imge kumesinde oznitelik cikarimi icin derin ogrenme modellerinden evrisimsel sini agi kullanilmistir. Hastaligin teshisi icin elde edilen oznitelikler farkli siniflandiricilar kullanilarak basarim karsilastirmalari yapilmistir. Karsilastirma sonucunda siniflandirma isleminde kullanilan, destek vektor makineleri ile % 95.8 gibi bir yuksek basari orani elde edilmistir. Zaturre gibi olumcul hastaliklarin erken teshisinde, derin ogrenme modellerin...
In recent years, deep learning models have been widely used on remote sensing images. Deep learni... more In recent years, deep learning models have been widely used on remote sensing images. Deep learning is held on remote sensing images as well as in every area; is to be able to perform better performance classification than existing approaches and to perceive the feature inferences on its own. In remote sensing, more studies are made especially on hyperspectral images. The most important reason for this is that it can carry a large number of data features. The large number of data features means that there are a large number of attributes for that image. The most important disadvantage of hyperspectral images is; due to the influence of the environment of the device which is shooting the image, various noises may occur. There may be a variety of information loss on this image. Various algorithms techniques have been developed to prevent these losses, while hyperspectral images have been better classified by deep learning models. Recent advances in deep learning models in technologica...
The transmission of weather information of a location at certain time intervals affects the livin... more The transmission of weather information of a location at certain time intervals affects the living conditions of the people there directly or indirectly. According to weather information, people shape their behavior in daily life. Besides, agricultural activities are carried out according to the weather conditions. Considering the importance of this subject, it is possible to make weather predictions based on the weather images in today’s technology exploiting the computer systems. However, the recent mention of the name of artificial intelligence technology in every field has made it compulsory for computer systems to benefit from this technology. The dataset used in the study has four classes: cloudy, rain, shine, and sunrise. In the study, GoogLeNet and VGG-16 models and the spiking neural network (SNN) were used together. The features extracted from GoogLeNet and VGG-16 models were combined and given to the SNNs as the input. As a result, the SNNs contributed to the success of classification with the proposed approach. The classification accuracy rates of cloudy, rain, shine, and sunrise classes were 98.48%, 97.58%, 97%, and 98.48%, respectively, together with SNN. Also, the use of SNNs in combination with deep learning models to obtain a successful result is proved in this study.
2019 23rd International Conference Electronics, 2019
In this study, the diagnosis of some diseases in the retina of the eye by using deep learning arc... more In this study, the diagnosis of some diseases in the retina of the eye by using deep learning architectures is intended to be diagnosed. Optical Coherence Tomography device from Choroidal Neovascularization, Diabetic Macular Edema, Drusen and healthy eye retinal images were examined. LeNet, AlexNet and Vgg16 architectures of deep learning were used. In each architecture, the hyper parameters were changed to diagnose these diseases. Results of the implementation showed that exhibit successful results in Vgg16 and AlexNet architecture. Dropout layer structure in AlexNet has been shown to reduce the loss by minimizing loss.
Abstract Unless adequate measures are taken for waste litter, the ecological balance may deterior... more Abstract Unless adequate measures are taken for waste litter, the ecological balance may deteriorate over time. The wastes disposed of the trash can be divided into two classes that are organic and recycling types. In recent years, artificial intelligence is frequently mentioned in all areas of technology. In this study, the dataset used for the classification of waste is reconstructed with the AutoEncoder network. The feature sets are then extracted using two datasets by Convolutional Neural Network (CNN) architectures and these feature sets are combined. The Ridge Regression (RR) method performed on the combined feature set reduced the number of features and also revealed the efficient features. Support Vector Machines (SVMs) were used as classifiers in all experiments. The most successful classification accuracy in the experiments was 99.95%. In this study, it is seen that the proposed approach is successful in the classification of waste types.
Invasive ductal carcinoma cancer, which invades the breast tissues by destroying the milk channel... more Invasive ductal carcinoma cancer, which invades the breast tissues by destroying the milk channels, is the most common type of breast cancer in women. Approximately, 80% of breast cancer patients have invasive ductal carcinoma and roughly 66.6% of these patients are older than 55 years. This situation points out a powerful relationship between the type of breast cancer and progressed woman age. In this study, the classification of invasive ductal carcinoma breast cancer is performed by using deep learning models, which is the sub-branch of artificial intelligence. In this scope, convolutional neural network models and the autoencoder network model are combined. In the experiment, the dataset was reconstructed by processing with the autoencoder model. The discriminative features obtained from convolutional neural network models were utilized. As a result, the most efficient features were determined by using the ridge regression method, and classification was performed using linear discriminant analysis. The best success rate of classification was achieved as 98.59%. Consequently, the proposed approach can be admitted as a successful model in the classification.
Physica A: Statistical Mechanics and its Applications, 2019
Abstract Road separations, intersections, and crosswalks, which are important components of highw... more Abstract Road separations, intersections, and crosswalks, which are important components of highways, are seen as significant areas for autonomous vehicles and advanced driver assistance systems because traffic accident occurrence rate is considerably high in these areas. In this study, an image processing method and a deep learning based approach on real images has been proposed in order to provide instant information for drivers and autonomous vehicles, or to develop warning systems as part of advanced driver assistance systems to prevent or minimize traffic accidents. The information is obtained from the classification of images belonging to the separations, intersections and crosswalks on the road using a new model and VggNet, AlexNet, LeNet based on Convolutional Neural Network(CNN). We have obtained high classification accuracy with our model based on CNN. The result of the study performed on different datasets showed that the proposed method is usable for driver assistance systems and an effective structure that can be used in many areas such as warning both vehicles and drivers.
2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 2018
Social networks are an increasingly popular medias today. On these medias, users are able to inte... more Social networks are an increasingly popular medias today. On these medias, users are able to interact with each other or share their views on an up-to-date topic, enabling them to have information about their own characters and worldviews. In this study, using the twitter data, the political tendency or parties supported by the users were predicted using data mining techniques.
2018 International Conference on Artificial Intelligence and Data Processing (IDAP)
Breast cancer is one of the most common cancer types diagnosed in the ladies worldwide. Statistic... more Breast cancer is one of the most common cancer types diagnosed in the ladies worldwide. Statistics indicate that breast cancer rate is about 12 % in all cancer cases in the world. Also, approximately 25% of women have breast cancer. Therefore, rapid and accurate analysis of medical images obtained with breast cancer is extremely important for diagnosis. Many methods have been used to classify breast cancer. One of the most important methods among these methods is deep learning-based methods. The most important reason for choosing the deep learning model for breast cancer diagnosis is that it can give faster and more accurate results than the existing methods. Because early diagnosis is always important. In this study, a total of 700 images, including benign and malign variants of breast cancer images, were analyzed using the convolutional neural network method. The data set used in this study is publicly accessible. As a result, the histopathologic images of the breast cancer features were extracted using the AlexNet method which is one of the deep learning approaches. The classification process was performed with Support Vector Machines and accuracy of 93.4% was achieved.
2019 1st International Informatics and Software Engineering Conference (UBMYK)
Brain hemorrhage refers to hemorrhage within the brain tissue or between the surrounding bone. Th... more Brain hemorrhage refers to hemorrhage within the brain tissue or between the surrounding bone. Therefore, head hemorrhage can lead to many dangerous consequences, especially brain hemorrhage. Early and correct intervention by experts in such cases is important for the patient’s life. In this study, computed tomography images of brain hemorrhage are classified by AlexNet which is one of the convolutional neural network models used recently in the biomedical field. In this scope, the data set is restructured with the autoencoder network model and heat maps of each image in the data set are extracted to improve the classification success. The number of images in the data set is then increased by approximately 10 times using the data augmentation technique. The classification process is performed using support vector machines. As a result, the best success rate in the classification was 98.57%. In conclusion, the proposed approach contributed to the classification of cerebral hemorrhage images.
2017 25th Signal Processing and Communications Applications Conference (SIU), 2017
In this study, an SVM-based system is proposed for the classification of facial expressions that ... more In this study, an SVM-based system is proposed for the classification of facial expressions that are represented in 3D. Distance based features are used as a feature vector, which are determined by the distances between the different key points on the image. Study was conducted on a subset (Happy, sadness, surprise) of Bosphorus 3D Face Database. 9 different fiducial points are used to calculate a total of 5 distance features. SVM classification was performed with K-fold cross validation thus mean classification performance of different training and test clusters were determined. %85 success rate has achieved as a result of the expression analysis performed on the 3D facial scans.
2020 28th Signal Processing and Communications Applications Conference (SIU), 2020
In recent years, deep learning methods have achieved high success as solution to problems in the ... more In recent years, deep learning methods have achieved high success as solution to problems in the computer vision. Especially, CNN algorithms that extract information from the image is widely applied in logo detection. In this case, the recognition of trademark in trademark applications or infringement has been one of the major problems in the literature in terms of companies. In this paper, the dataset containing the logos of banks acquired from public domain images was collected in order to perform logo recognition, by using Faster R-CNN, an approach for the recognition of the bank logo in the video have been developed and as a result average accuracy of %98 was obtained.
Son zamanlarda goruntu isleme ile ilgili gelismeler, hizla gelisen teknolojik sistemlerin ilerlem... more Son zamanlarda goruntu isleme ile ilgili gelismeler, hizla gelisen teknolojik sistemlerin ilerlemesinde katkida bulunmustur. Ozellikle saglik alanindaki goruntu isleme ile ilgili calismalar populerligini daha da artirmistir. Gerek tibbi goruntuler olsun gerekse diger alandaki goruntuler olsun, mevcut yontemler uzerinde basari saglatilmasina ragmen; derin ogrenme modeli, mevcut yontemlere kiyasla zaman ve performans acisindan daha fazla katkida bulunan bir modeldir. Mevcut yontemler ile tek katmanli goruntuler uzerinden islem yapiliyorken, derin ogrenme modeliyle, cok katmanli goruntuler uzerinden performansi yuksek sonuclar alinabilmektedir. Derin ogrenmenin en onemli ozelligi, goruntu uzerindeki islemleri tek bir sefer de isleme tabi tutan ve el ile girilmesi gereken parametreleri kendi kendine kesif edebilmesidir. Ayrica teknoloji firmalarinin da derin ogrenmeye yonelmesi, kendi aralarinda rekabet gucunu artirdigi gibi, bilimsel anlamda derin ogrenme uzerine kurduklari yontemler, ...
Zaturre Hastaligi, insanin hayatinin herhangi bir doneminde karsilasabilecegi hastaliklardan biri... more Zaturre Hastaligi, insanin hayatinin herhangi bir doneminde karsilasabilecegi hastaliklardan biridir. Enfeksiyon hastaliklarinin yaklasik %18’ini zaturre hastaligi olusturmaktadir. Bu hastalik ilerleyen bazi durumlarda olume sebep olabilmektedir. Tibbi olarak zaturre teshisini kesin olarak konulabilmesi icin akciger rontgen goruntulerinin bir doktor tarafindan incelenmesi gereklidir. Bu calismada, zaturre hastaliginin teshisi icin gelistirilen tanima sistemi icin erisime acik olan akciger rontgen goruntulerinden faydalanilmistir. Elde edilen imge kumesinde oznitelik cikarimi icin derin ogrenme modellerinden evrisimsel sini agi kullanilmistir. Hastaligin teshisi icin elde edilen oznitelikler farkli siniflandiricilar kullanilarak basarim karsilastirmalari yapilmistir. Karsilastirma sonucunda siniflandirma isleminde kullanilan, destek vektor makineleri ile % 95.8 gibi bir yuksek basari orani elde edilmistir. Zaturre gibi olumcul hastaliklarin erken teshisinde, derin ogrenme modellerin...
In recent years, deep learning models have been widely used on remote sensing images. Deep learni... more In recent years, deep learning models have been widely used on remote sensing images. Deep learning is held on remote sensing images as well as in every area; is to be able to perform better performance classification than existing approaches and to perceive the feature inferences on its own. In remote sensing, more studies are made especially on hyperspectral images. The most important reason for this is that it can carry a large number of data features. The large number of data features means that there are a large number of attributes for that image. The most important disadvantage of hyperspectral images is; due to the influence of the environment of the device which is shooting the image, various noises may occur. There may be a variety of information loss on this image. Various algorithms techniques have been developed to prevent these losses, while hyperspectral images have been better classified by deep learning models. Recent advances in deep learning models in technologica...
The transmission of weather information of a location at certain time intervals affects the livin... more The transmission of weather information of a location at certain time intervals affects the living conditions of the people there directly or indirectly. According to weather information, people shape their behavior in daily life. Besides, agricultural activities are carried out according to the weather conditions. Considering the importance of this subject, it is possible to make weather predictions based on the weather images in today’s technology exploiting the computer systems. However, the recent mention of the name of artificial intelligence technology in every field has made it compulsory for computer systems to benefit from this technology. The dataset used in the study has four classes: cloudy, rain, shine, and sunrise. In the study, GoogLeNet and VGG-16 models and the spiking neural network (SNN) were used together. The features extracted from GoogLeNet and VGG-16 models were combined and given to the SNNs as the input. As a result, the SNNs contributed to the success of classification with the proposed approach. The classification accuracy rates of cloudy, rain, shine, and sunrise classes were 98.48%, 97.58%, 97%, and 98.48%, respectively, together with SNN. Also, the use of SNNs in combination with deep learning models to obtain a successful result is proved in this study.
2019 23rd International Conference Electronics, 2019
In this study, the diagnosis of some diseases in the retina of the eye by using deep learning arc... more In this study, the diagnosis of some diseases in the retina of the eye by using deep learning architectures is intended to be diagnosed. Optical Coherence Tomography device from Choroidal Neovascularization, Diabetic Macular Edema, Drusen and healthy eye retinal images were examined. LeNet, AlexNet and Vgg16 architectures of deep learning were used. In each architecture, the hyper parameters were changed to diagnose these diseases. Results of the implementation showed that exhibit successful results in Vgg16 and AlexNet architecture. Dropout layer structure in AlexNet has been shown to reduce the loss by minimizing loss.
Abstract Unless adequate measures are taken for waste litter, the ecological balance may deterior... more Abstract Unless adequate measures are taken for waste litter, the ecological balance may deteriorate over time. The wastes disposed of the trash can be divided into two classes that are organic and recycling types. In recent years, artificial intelligence is frequently mentioned in all areas of technology. In this study, the dataset used for the classification of waste is reconstructed with the AutoEncoder network. The feature sets are then extracted using two datasets by Convolutional Neural Network (CNN) architectures and these feature sets are combined. The Ridge Regression (RR) method performed on the combined feature set reduced the number of features and also revealed the efficient features. Support Vector Machines (SVMs) were used as classifiers in all experiments. The most successful classification accuracy in the experiments was 99.95%. In this study, it is seen that the proposed approach is successful in the classification of waste types.
Invasive ductal carcinoma cancer, which invades the breast tissues by destroying the milk channel... more Invasive ductal carcinoma cancer, which invades the breast tissues by destroying the milk channels, is the most common type of breast cancer in women. Approximately, 80% of breast cancer patients have invasive ductal carcinoma and roughly 66.6% of these patients are older than 55 years. This situation points out a powerful relationship between the type of breast cancer and progressed woman age. In this study, the classification of invasive ductal carcinoma breast cancer is performed by using deep learning models, which is the sub-branch of artificial intelligence. In this scope, convolutional neural network models and the autoencoder network model are combined. In the experiment, the dataset was reconstructed by processing with the autoencoder model. The discriminative features obtained from convolutional neural network models were utilized. As a result, the most efficient features were determined by using the ridge regression method, and classification was performed using linear discriminant analysis. The best success rate of classification was achieved as 98.59%. Consequently, the proposed approach can be admitted as a successful model in the classification.
Physica A: Statistical Mechanics and its Applications, 2019
Abstract Road separations, intersections, and crosswalks, which are important components of highw... more Abstract Road separations, intersections, and crosswalks, which are important components of highways, are seen as significant areas for autonomous vehicles and advanced driver assistance systems because traffic accident occurrence rate is considerably high in these areas. In this study, an image processing method and a deep learning based approach on real images has been proposed in order to provide instant information for drivers and autonomous vehicles, or to develop warning systems as part of advanced driver assistance systems to prevent or minimize traffic accidents. The information is obtained from the classification of images belonging to the separations, intersections and crosswalks on the road using a new model and VggNet, AlexNet, LeNet based on Convolutional Neural Network(CNN). We have obtained high classification accuracy with our model based on CNN. The result of the study performed on different datasets showed that the proposed method is usable for driver assistance systems and an effective structure that can be used in many areas such as warning both vehicles and drivers.
2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 2018
Social networks are an increasingly popular medias today. On these medias, users are able to inte... more Social networks are an increasingly popular medias today. On these medias, users are able to interact with each other or share their views on an up-to-date topic, enabling them to have information about their own characters and worldviews. In this study, using the twitter data, the political tendency or parties supported by the users were predicted using data mining techniques.
2018 International Conference on Artificial Intelligence and Data Processing (IDAP)
Breast cancer is one of the most common cancer types diagnosed in the ladies worldwide. Statistic... more Breast cancer is one of the most common cancer types diagnosed in the ladies worldwide. Statistics indicate that breast cancer rate is about 12 % in all cancer cases in the world. Also, approximately 25% of women have breast cancer. Therefore, rapid and accurate analysis of medical images obtained with breast cancer is extremely important for diagnosis. Many methods have been used to classify breast cancer. One of the most important methods among these methods is deep learning-based methods. The most important reason for choosing the deep learning model for breast cancer diagnosis is that it can give faster and more accurate results than the existing methods. Because early diagnosis is always important. In this study, a total of 700 images, including benign and malign variants of breast cancer images, were analyzed using the convolutional neural network method. The data set used in this study is publicly accessible. As a result, the histopathologic images of the breast cancer features were extracted using the AlexNet method which is one of the deep learning approaches. The classification process was performed with Support Vector Machines and accuracy of 93.4% was achieved.
2019 1st International Informatics and Software Engineering Conference (UBMYK)
Brain hemorrhage refers to hemorrhage within the brain tissue or between the surrounding bone. Th... more Brain hemorrhage refers to hemorrhage within the brain tissue or between the surrounding bone. Therefore, head hemorrhage can lead to many dangerous consequences, especially brain hemorrhage. Early and correct intervention by experts in such cases is important for the patient’s life. In this study, computed tomography images of brain hemorrhage are classified by AlexNet which is one of the convolutional neural network models used recently in the biomedical field. In this scope, the data set is restructured with the autoencoder network model and heat maps of each image in the data set are extracted to improve the classification success. The number of images in the data set is then increased by approximately 10 times using the data augmentation technique. The classification process is performed using support vector machines. As a result, the best success rate in the classification was 98.57%. In conclusion, the proposed approach contributed to the classification of cerebral hemorrhage images.
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Papers by Burhan Ergen