Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Yildirim, Ozal(19)
- Utku, Anıl(11)
- Talo, Muhammed(10)
- Utku, Anil(10)
- Baloglu, Ulas Baran(9)
- Acharya, U. Rajendra(7)
- Ergen, Burhan(7)
- Doğan, Gürkan(5)
- Karakose, Mehmet(5)
- Yilmazer, Merve(5)
- Can, Umit(4)
- Demir, Yakup(4)
- Imak, Andaç(4)
- Acharya, U Rajendra(3)
- Aydin, Galip(3)
- Ertargin, Merve(3)
- Gunay, Mihriban(3)
- Tan, Ru-San(3)
- Yıldırım, Özal(3)
- Akcayol, M. Ali(2)
- Ay, Betul(2)
- Barut, Abdulkadir(2)
- Iliev, Teodor(2)
- Karabatak, Murat(2)
- Kaya, Sema Kayapinar(2)
- Korkmaz, Adem(2)
- Kosunalp, Selahattin(2)
- Kutlu, Banu(2)
- Orhan, Ahmet(2)
- Pilatin, Abdulmuttalip(2)
- Rajendra Acharya, U.(2)
- Sengur, Abdulkadir(2)
- Tan, Ru San(2)
- Türkoğlu, Muammer(2)
- Çelebi, Adalet(2)
- Çelik, Yusuf(2)
- Şengür, Abdulkadir(2)
- Agdas, Mehmet T.(1)
- Agdas, Mehmet Tevfik(1)
- Akcayol, Muhammet Ali(1)
Yayınlar
- MakaleAçık Erişim2023Scopus SJRQ2ResMIBCU-Net: an encoder–decoder network with residual blocks, modified inverted residual block, and bi-directional ConvLSTM for impacted tooth segmentation in panoramic X-ray images
Imak, Andaç; Çelebi, Adalet; Polat, Onur; Türkoğlu, Muammer; Şengür, Abdulkadir
Objective: Impacted tooth is a common problem that can occur at any age, causing tooth decay, root resorption, and pain in the later stages. In recent years, major advances have been made in medical imaging segmentation using deep convolutional neural network-based networks. In this study, we report on the development of an artificial intelligence system for the automatic identification of impacted tooth from panoramic dental X-ray images. Methods: Among existing networks, in medical imaging segmentation, U-Net architectures are widely implemented. In this article, for dental X-ray image segmentation, blocks and convolutional block structures using inverted residual blocks are upgraded by taking advantage of U-Net’s network capacity-intensive connections. At the same time, we propose a method for jumping connections in which bi-directional convolution long short-term memory is used instead of a simple connection. Assessment of the proposed artificial intelligence model performance was evaluated with accuracy, F1-score, intersection over union, and recall. Results: In the proposed method, experimental results are obtained with 99.82% accuracy, 91.59% F1-score, 84.48% intersection over union, and 90.71% recall. Conclusion: Our findings show that our artificial intelligence system could help with future diagnostic support in clinical practice.
AtıfScopus21OpenAlex23
İncele - MakaleYalnız künye2023Scopus SJRQ1An efficient hybrid weather prediction model based on deep learning
Utku, A.; Can, U.
Weather events directly affect human activities. In particular, extreme weather events with global warming, forest fires, and high air temperatures that cause drought make human life difficult. Effective and accurate weather prediction models are needed to take precautions against such climatic events. Therefore, it is essential to develop models that make precise weather predictions. Technological developments contributed significantly to developing successful deep learning-based weather prediction models. With a high success rate, this study proposed a hybrid weather prediction model based on Convolutional Neural Networks and Recurrent Neural Networks models. The proposed hybrid model was applied to the Jena dataset, which contains 14-parameter, large-scale meteorological data that were never utilized for weather prediction. The experimental results were compared with popular deep learning, machine learning, and statistical methods such as Auto-Regressive Integrated Moving Average, Convolution Neural Networks, Long-Short Term Memory, Multilayer Perceptron, Random Forest, Recurrent Neural Networks, and Support Vector Machine. As a result of these comparisons, the proposed hybrid model obtained the best prediction result for all metrics. For example, according to the weather prediction results for Jena, Germany, the proposed hybrid model got the results of Mean Squared Error: 0.035, Root-Mean-Squared Error: 0.189, Mean Absolute Error: 0.126, and R-Squared: 0.987.
AtıfScopus15WoS11OpenAlex11İncele - MakaleAçık Erişim2023Scopus SJRQ1Deep learning based hybrid prediction model for predicting the spread of COVID-19 in the world's most populous countries
Utku, Anil
COVID-19 has a disease and health phenomenon and has sociological and economic adverse effects. Accurate prediction of the spread of the epidemic will help in the planning of health management and the development of economic and sociological action plans. In the literature, there are many studies to analyse and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyse the cross-country spread in the world's most populous countries. In this study, it was aimed to predict the spread of the COVID-19 epidemic. The motivation of this study is to reduce the workload of health workers, take preventive measures and optimize health processes by predicting the spread of the COVID-19 epidemic. A hybrid deep learning model was developed to predict and analyse COVID-19 cross-country spread and a case study was carried out for the world's most populous countries. The developed model was tested extensively using RMSE, MAE and R2. The experimental results showed that the developed model was more successful in predicting and analysis of COVID-19 cross-country spread in the world's most populous countries than LR, RF, SVM, MLP, CNN, GRU, LSTM and base CNN-GRU. In the developed model, CNN performs convolution and pooling operations to extract spatial features from the input data. GRU provides learning of long-term and non-linear relationships inferred by CNN. The developed hybrid model was more successful than the other models compared, as it enabled the effective features of the CNN and GRU models to be used together. The prediction and analysis of the cross-country spread of COVID-19 in the world's most populated countries can be presented as a novelty of this study.
AtıfScopus23WoS20OpenAlex31İncele - MakaleAçık Erişim2023Scopus SJRQ1DEEP LEARNING BASED AN EFFICIENT HYBRID PREDICTION MODEL FOR COVID-19 CROSS-COUNTRY SPREAD AMONG E7 AND G7 COUNTRIES
Utku, Anil
The COVID-19 pandemic has caused the death of many people around the world and has also caused economic problems for all countries in the world. In the literature, there are many studies to analyze and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyze the cross-country spread in the world. In this study, a deep learning based hybrid model was developed to predict and analysis of COVID-19 cross-country spread and a case study was carried out for Emerging Seven (E7) and Group of Seven (G7) countries. It is aimed to reduce the workload of healthcare professionals and to make health plans by predicting the daily number of COVID-19 cases and deaths. Developed model was tested extensively using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R Squared (R2). The experimental results showed that the developed model was more successful to predict and analysis of COVID-19 cross-country spread in E7 and G7 countries than Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The developed model has R2 value close to 0.9 in predicting the number of daily cases and deaths in the majority of E7 and G7 countries.
AtıfScopus9OpenAlex11İncele - MakaleAçık Erişim2023Scopus SJRQ2Extracting low dimensional representations from large size whole slide images using deep convolutional autoencoders
Çelik, Yusuf; Karabatak, Murat
Background and objective: Artificial intelligence-based analysis of medical images has recently become a trendy field of study. The most significant effect on these systems to produce reliable and high-performance results is the amount of accessed data. Although whole-slide images (WSI), one of the current imaging techniques used in histopathology, storage costs are very expensive. Therefore, low-dimensional visuals representing of WSIs is an essential field of study. Methods: In this study, deep auto-encoder-based models are designed to create low dimensional representations for WSI. The size reduction was performed for the input images in different sizes, at the ratios of 1: 3, 1: 6, and 1: 12, respectively. Results: Similarity index measure values were obtained as high as 0.957 and 0.938, respectively, using 128 × 128 × 3 dimensional patches in the size reduction process of 1: 3 and 1:12 on test WSI. Conclusion: The proposed model has a structure that can be applied in compression of WSIs and secure data transfer. By using these representations, both storage costs and the high-level hardware costs required by deep learning algorithms can be significantly reduced.
AtıfScopus3WoS2OpenAlex4İncele - MakaleYalnız künye2023Scopus SJRQ2Detection of hateful twitter users with graph convolutional network model
Utku, Anıl; Can, Umit; Aslan, Serpil
Today, hate speech is widespread and persistent in various forms on social networking platforms, targeting different minority groups. These attacks can be carried out using various factors such as racial, religious, gender, and physical disability, etc. Considering the number of people and their interactions, social networks are the most important channels through which these discourses spread. The social network structure is considered a set of nodes and edges and is very suitable for the graph structure. The multidimensional structure of social networks carries social network data from Euclidean space to non-Euclidean space. In non-Euclidean space, the graph structure is used to represent data effectively. In this respect, solving the hate speech problem with graph-based methods in a complex dimensional space can produce more impressive results. In this study, a powerful method based on the Graph Convolutional Network (GCN) model, which is rarely used in this field, was proposed for the detection of hateful Twitter users in social networks. Well-known machine learning methods were used to measure the performance of this method. According to the results obtained, the proposed GCN model gave the most successful result.
AtıfScopus15WoS10OpenAlex16İncele - MakaleYalnız künye2023Scopus SJRQ2Deep learning based an effective hybrid model for water quality assessment
Utku, Anıl; Utku, Esen Damla; Kutlu, Banu
Water, which is very important for life and civilizations on Earth, has been a source of life for all living things. However, freshwater resources gradually decrease due to climate change, pollution, and population growth. Water pollution is the quality changes that occur due to various activities of people, change the chemical, biological, and physical properties of water, restrict or prevent its use, and disrupt ecological balances. The water quality criteria determined to keep this pollution under control ensure the safe use of water. Water quality observations have gained more importance today due to intense environmental concerns and water pollution. This situation revealed the necessity of water quality assessment for water source use. In this study, the CNN-LSTM-based hybrid model was proposed to assess water quality. The proposed model was compared with AdaBoost, DT, GNB, kNN, LGBM, RF, and LSTM according to accuracy, precision, recall, F-score, and AUC. The proposed model has 98.81% accuracy, 99.03% precision, 99.65% recall, 99.33% F-score, and 93% AUC. Practitioner Points: CNN-LSTM hybrid model was developed to water quality assessment. The proposed model is compared with established techniques like AdaBoost, DT, GNB, kNN, LGBM, RF, and LSTM. The developed model has 98.81% classification accuracy. Experimental results show that the developed model will ensure the use of safe water according to water quality criteria.
AtıfScopus7WoS4OpenAlex8İncele - MakaleAçık Erişim2022Scopus SJRQ3A New Rail Surface Defects Detection Approach Using 3D Laser Cameras Based on ResNet50
Santur, Yunus; Yilmazer, Merve; Karakose, Mehmet; Akin, Erhan
Rail transportation systems, which are used as one of the most common means of transportation worldwide, should be regularly inspected to prevent accidents that may occur. The rail condition monitoring can be performed in high accuracy and real time using computer vision, deep learning algorithms today. In this study, a new deep learning based approach using 3D laser cameras for rail inspection is presented. In the proposed approach, two 3D laser cameras placed on a real train, seeing the rail line from the left and right surfaces were used. These data consisting of sensitive distance value constitute the input data of the ResNet50 transfer learning model. The training was carried out on Nvidia Cuda supported graphics processing units using ResNet50 Convolutional Neural Network. During the test phase, the operation speed and accuracy rate of the method was measured by repeating the process on real-time rail profiles. The accuracy rate was calculated as 94%. As a result a new approach is presented based on deep learning using 3D laser cameras for rail inspection is presented.
AtıfScopus12WoS8OpenAlex9İncele - MakaleAçık Erişim2022Scopus SJRQ2Using network traffic analysis deep learning based Android malware detection; [Aǧ trafiǧi analizi ile derin öǧrenme tabanli Android kötücül yazilim tespiti]
Utku, Anil
Mobile devices, which are becoming more and more widespread today, have turned into hand-held computers thanks to the multimedia communication and applications. Today, the multimedia applications have been supported by traditional mobile phones. With their increasing functionality, mobile devices are used for many purposes such as enriched internet experience, financial transactions, access to social media platforms, sharing, music and video. Performing transactions where sensitive personal data transfers such as banking and shopping are carried out on mobile devices make mobile devices the target of attackers. In this study, a deep learning based malware detection system has been developed based on the interactions of mobile applications on the network. The developed LSTM-based deep learning model has been analysed comparatively with NB, RF, SVM, MLP, CNN, RNN and GRU using accuracy, precision, recall and F-1 metrics. The experimental results showed that the developed LSTM-based deep learning model is more successful in malware detection than others with 95% accuracy.
AtıfScopus4WoS3OpenAlex4TR Dizin1
İncele - MakaleAçık Erişim2022Scopus SJRQ1DEEP LEARNING BASED A COMPREHENSIVE ANALYSIS FOR WASTE PREDICTION
Utku, Anıl; Kaya, Sema Kayapinar
In its simplest definition, waste can be defined as any substance that is used, not needed and causes harm to the environment. Waste management covers control activities such as prevention of the formation of waste, reuse, separation according to its characteristics and type, storage, transportation, recycling and disposal. The main purpose of waste management is to leave a livable world to future generations, to create a sustainable environment, to protect natural resources, to save energy and costs, to reduce the rate of pollution and the amount of hazardous waste. In today's world where urbanization and industrialization rates are increasing, waste management is gaining importance. The aim of this study is to utilize waste data from Istanbul, Turkey's largest and fastest growing city, to estimate waste amount using a constructed Long Short-Term Memory (LSTM) based deep learning model. The developed LSTM-based model has been compared in practice with k-Nearest Neighbors (kNN), random forest (RF), Support Vector Machine (SVM), multi-layered perceptron (MLP) and Gated Recurrent Unit (GRU). As a result of the comparative and comprehensive analyzes, the experimental results showed that the developed LSTM-based deep learning method is more successful in the waste prediction problem than the other compared models.
AtıfScopus13OpenAlex13İncele - MakaleYalnız künye2020Scopus SJRQ1A deep convolutional neural network model for automated identification of abnormal EEG signals
Yıldırım, Özal; Baloglu, Ulas Baran; Acharya, U. Rajendra
Electroencephalogram (EEG) is widely used to monitor the brain activities. The manual examination of these signals by experts is strenuous and time consuming. Hence, machine learning techniques can be used to improve the accuracy of detection. Nowadays, deep learning methodologies have been used in medical field to diagnose the health conditions precisely and aid the clinicians. In this study, a new deep one-dimensional convolutional neural network (1D CNN) model is proposed for the automatic recognition of normal and abnormal EEG signals. The proposed model is a complete end-to-end structure which classifies the EEG signals without requiring any feature extraction. In this study, we have used the EEG signals from temporal to occipital (T5–O1) single channel obtained from Temple University Hospital EEG Abnormal Corpus (v2.0.0) EEG dataset to develop the 1D CNN model. Our developed model has yielded the classification error rate of 20.66% in classifying the normal and abnormal EEG signals.
AtıfScopus164OpenAlex211İncele - MakaleAçık Erişim2020Scopus SJRQ11D-CADCapsNet: One dimensional deep capsule networks for coronary artery disease detection using ECG signals
Butun, Ertan; Yildirim, Ozal; Talo, Muhammed; Tan, Ru-San; Rajendra Acharya, U.
Purpose: Cardiovascular disease (CVD) is a leading cause of death globally. Electrocardiogram (ECG), which records the electrical activity of the heart, has been used for the diagnosis of CVD. The automated and robust detection of CVD from ECG signals plays a significant role for early and accurate clinical diagnosis. The purpose of this study is to provide automated detection of coronary artery disease (CAD) from ECG signals using capsule networks (CapsNet). Methods: Deep learning-based approaches have become increasingly popular in computer aided diagnosis systems. Capsule networks are one of the new promising approaches in the field of deep learning. In this study, we used 1D version of CapsNet for the automated detection of coronary artery disease (CAD) on two second (95,300) and five second-long (38,120) ECG segments. These segments are obtained from 40 normal and 7 CAD subjects. In the experimental studies, 5-fold cross validation technique is employed to evaluate performance of the model. Results: The proposed model, which is named as 1D-CADCapsNet, yielded a promising 5-fold diagnosis accuracy of 99.44% and 98.62% for two- and five-second ECG signal groups, respectively. We have obtained the highest performance results using 2 s ECG segment than the state-of-art studies reported in the literature. Conclusions: 1D-CADCapsNet model automatically learns the pertinent representations from raw ECG data without using any hand-crafted technique and can be used as a fast and accurate diagnostic tool to help cardiologists.
AtıfScopus100OpenAlex96İncele - MakaleAçık Erişim2020Scopus SJRQ1Automated detection of COVID-19 cases using deep neural networks with X-ray images
Ozturk, Tulin; Talo, Muhammed; Yildirim, Eylul Azra; Baloglu, Ulas Baran; Yildirim, Ozal; Rajendra Acharya, U.
The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It has caused a devastating effect on both daily lives, public health, and the global economy. It is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients. The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. Application of advanced artificial intelligence (AI) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages. In this study, a new model for automatic COVID-19 detection using raw chest X-ray images is presented. The proposed model is developed to provide accurate diagnostics for binary classification (COVID vs. No-Findings) and multi-class classification (COVID vs. No-Findings vs. Pneumonia). Our model produced a classification accuracy of 98.08% for binary classes and 87.02% for multi-class cases. The DarkNet model was used in our study as a classifier for the you only look once (YOLO) real time object detection system. We implemented 17 convolutional layers and introduced different filtering on each layer. Our model (available at (https://github.com/muhammedtalo/COVID-19)) can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients.
AtıfScopus2286OpenAlex2595İncele - MakaleAçık Erişim2020Scopus SJRQ1Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images
Celik, Yusuf; Talo, Muhammed; Yildirim, Ozal; Karabatak, Murat; Acharya, U Rajendra
Advances in artificial intelligence technologies have made it possible to obtain more accurate and reliable results using digital images. Due to the advances in digital histopathological images obtained using whole slide image (WSI) scanners, automated analysis of digital images by computer support systems has become interesting. In particular, deep learning architectures, are one of the preferred approaches in the analysis of digital histopathology images. The deeper networks trained on large amounts of image data are adapted for different tasks using transfer learning technique. In this study, automated detection of invasive ductal carcinoma (IDC), which is the most common subtype of breast cancers, is proposed using deep transfer learning technique. We have used deep learning pre-trained models, ResNet-50 and DenseNet-161 for the IDC detection task. The public histopathology dataset containing 277,524 image patches were used in our experimental studies. As a result of training on the last layers of pre-trained deep networks, DenseNet-161 model has yielded F-sore of 92.38% and balanced accuracy value of 91.57%. Similarly, we have obtained F-score of 94.11% and balanced accuracy value of 90.96% using ResNet-50 architecture. In addition, our developed model is validated using the publicly available BreakHis breast cancer dataset and obtained promising results in classifying magnification independent histopathology images into benign and malignant classes. Our developed system obtained the highest classification performance as compared to the state-of-art techniques and is ready to be tested with more diverse huge databases.
AtıfScopus297OpenAlex318İncele - BildiriYalnız künye2020Classification of Hand-Drawn Basic Circuit Components Using Convolutional Neural Networks
Gunay, Mihriban; Koseoglu, Murat; Yildirim, Ozal
In this paper, the Convolutional Neural Network (CNN) architecture, which is one of the deep learning architectures, is used to classify the basic circuit components drawn by hand. During the training and testing stages of the model, a new dataset containing images of 863 circuit components manually drawn by different people is created. The data set contains images of four different classes of circuit components such as resistor, inductor, capacitor and voltage source. All images have been fixed to the same size and converted to grayscale to increase recognition performance and reduce process complexity. In the study, training for four classes is performed with CNN architecture. Based on the CNN architecture, four new CNN models are employed with different the number of layers. The training and validation results of these models are compared separately, the model with the highest training and validation performance is observed with four layer CNN model (CNN-4). This model obtained 84.41% accuracy rate at classification task.
AtıfScopus16WoS5OpenAlex17İncele - Derleme MakaleYayıncıda açık erişim2020Scopus SJRQ1Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review
Murat, Fatma; Yildirim, Ozal; Talo, Muhammed; Baloglu, Ulas Baran; Demir, Yakup; Acharya, U. Rajendra
Deep learning models have become a popular mode to classify electrocardiogram (ECG) data. Investigators have used a variety of deep learning techniques for this application. Herein, a detailed examination of deep learning methods for ECG arrhythmia detection is provided. Approaches used by investigators are examined, and their contributions to the field are detailed. For this purpose, journal papers have been surveyed according to the methods used. In addition, various deep learning models and experimental studies are described and discussed. A five-class ECG dataset containing 100,022 beats was then utilized for further analysis of deep learning techniques. The constructed models were examined with this dataset, and results are presented. This study therefore provides information concerning deep learning approaches used for arrhythmia classification, and suggestions for further research in this area.
AtıfScopus318OpenAlex328İncele - BildiriYalnız künye2019Pneumonia Detection from Radiography Images using Convolutional Neural Networks
Talo, Muhammed
Pneumonia continues to be the leading cause of child mortality in children under the age of five, and 2,400 children, most of whom are babies under 2 years of age, die from pneumonia. In this study, an automated detection system is proposed for the diagnosis of pneumonia with chest radiography images. With the transfer learning technique, ResNet-152 convolutional neural network was customized to recognize pneumonia from radiography images. With this customized architecture, a recognition success of 97.4% was obtained in the detection of pneumonia disease without any preprocessing of raw data or manual feature extraction on radiography images. This model, which was proposed for the detection of pneumonia, found to be more successful when compared with the other successful studies in the literature.
AtıfWoS6OpenAlex16İncele - MakaleYalnız künye2019Scopus SJRQ4CONVOLUTIONAL LONG-SHORT TERM MEMORY NETWORKS MODEL FOR LONG DURATION EEG SIGNAL CLASSIFICATION
Baloglu, Ulas Baran; Yildirim, Ozal
Background and objective: Deep learning structures have recently achieved remarkable success in the field of machine learning. Convolutional neural networks (CNN) in image processing and long-short term memory (LSTM) in the time-series analysis are commonly used deep learning algorithms. Healthcare applications of deep learning algorithms provide important contributions for computer-aided diagnosis research. In this study, convolutional long-short term memory (CLSTM) network was used for automatic classification of EEG signals and automatic seizure detection. Methods: A new nine-layer deep network model consisting of convolutional and LSTM layers was designed. The signals processed in the convolutional layers were given as an input to the LSTM network whose outputs were processed in densely connected neural network layers. The EEG data is appropriate for a model having 1-D convolution layers. A bidirectional model was employed in the LSTM layer. Results: Bonn University EEG database with five different datasets was used for experimental studies. In this database, each dataset contains 23.6 s duration 100 single channel EEG segments which consist of 4097 dimensional samples (173.61 Hz). Eight two-class and three three-class clinical scenarios were examined. When the experimental results were evaluated, it was seen that the proposed model had high accuracy on both binary and ternary classification tasks. Conclusions: The proposed end-to-end learning structure showed a good performance without using any hand-crafted feature extraction or shallow classifiers to detect the seizures. The model does not require filtering, and also automatically learns to filter the input as well. As a result, the proposed model can process long duration EEG signals without applying segmentation, and can detect epileptic seizures automatically by using the correlation of ictal and interictal signals of raw data.
AtıfWoS17OpenAlex19İncele - MakaleYalnız künye2019Scopus SJRQ3Regp: A new pooling algorithm for deep convolutional neural networks
Yildirim, O.; Baloglrf, U.B.
In this paper, we propose a new pooling method for deep convolu-tional neural networks. Previously introduced pooling methods either have very simple assumptions or they depend on stochastic events. Different from those methods, RegP pooling intensely investigates the input data. The main idea of this approach is finding the most distinguishing parts in regions of the input by investigating neighborhood regions to construct the pooled representation. RegP pooling improves the efficiency of the learning process, which is clearly visible in the experimental results. Further, the proposed pooling method outperformed other widely used hand-crafted pooling methods on several benchmark datasets.
AtıfScopus8WoS6OpenAlex9İncele - MakaleYalnız künye2019Scopus SJRQ1Classification of myocardial infarction with multi-lead ECG signals and deep CNN
Baloglu, Ulas Baran; Talo, Muhammed; Yildirim, Ozal; Tan, Ru San; Acharya, U Rajendra
Myocardial infarction (MI), commonly known as heart attack, causes irreversible damage to heart muscles and even leads to death. Rapid and accurate diagnosis of MI is critical to avoid death. Blood tests and electrocardiogram (ECG) signals are used to diagnose acute MI. However, for an increase in blood enzyme values, a certain time must pass after the attack. This time lag may delay MI diagnosis. Hence, ECG diagnosis is still very important. Manual ECG interpretation requires expertise and is prone to inter-observer variability. Therefore, computer aided diagnosis may be useful in automatic detection of MI on ECG. In this study, a deep learning model with an end-to-end structure on the standard 12-lead ECG signal for the diagnosis of MI is proposed. For this purpose, the most commonly used technique, convolutional neural network (CNN) is used. Our trained CNN model with the proposed architecture yielded impressive accuracy and sensitivity performance over 99.00% for MI diagnosis on all ECG lead signals. Thus, the proposed model has the potential to provide high performance on MI detection which can be used in wearable technologies and intensive care units.
AtıfScopus457OpenAlex476İncele