Yazar "Durdu, Akif" seçeneğine göre listele

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    CNN-based transfer learning-BiLSTM network: A novel approach for COVID-19 infection detection
    (Elsevier, 2021) Aslan, Muhammet Fatih; Unlersen, Muhammed Fahri; Sabanci, Kadir; Durdu, Akif
    Coronavirus disease 2019 (COVID-2019), which emerged in Wuhan, China in 2019 and has spread rapidly all over the world since the beginning of 2020, has infected millions of people and caused many deaths. For this pandemic, which is still in effect, mobilization has started all over the world, and various restrictions and precautions have been taken to prevent the spread of this disease. In addition, infected people must be identified in order to control the infection. However, due to the inadequate number of Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, Chest computed tomography (CT) becomes a popular tool to assist the diagnosis of COVID-19. In this study, two deep learning architectures have been proposed that automatically detect positive COVID-19 cases using Chest CT X-ray images. Lung segmentation (preprocessing) in CT images, which are given as input to these proposed architectures, is performed automatically with Artificial Neural Networks (ANN). Since both architectures contain AlexNet architecture, the recommended method is a transfer learning application. However, the second proposed architecture is a hybrid structure as it contains a Bidirectional Long Short-Term Memories (BiLSTM) layer, which also takes into account the temporal properties. While the COVID-19 classification accuracy of the first architecture is 98.14%, this value is 98.70% in the second hybrid architecture. The results prove that the proposed architecture shows outstanding success in infection detection and, therefore this study contributes to previous studies in terms of both deep architectural design and high classification success. (C) 2020 Elsevier B.V. All rights reserved.
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    COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization
    (Pergamon-Elsevier Science Ltd, 2022) Aslan, Muhammet Fatih; Sabanci, Kadir; Durdu, Akif; Unlersen, Muhammed Fahri
    The coronavirus outbreak 2019, called COVID-19, which originated in Wuhan, negatively affected the lives of millions of people and many people died from this infection. To prevent the spread of the disease, which is still in effect, various restriction decisions have been taken all over the world. In addition, the number of COVID-19 tests has been increased to quarantine infected people. However, due to the problems encountered in the supply of RTPCR tests and the ease of obtaining Computed Tomography and X-ray images, imaging-based methods have become very popular in the diagnosis of COVID-19. Therefore, studies using these images to classify COVID-19 have increased. This paper presents a classification method for computed tomography chest images in the COVID-19 Radiography Database using features extracted by popular Convolutional Neural Networks (CNN) models (AlexNet, ResNet18, ResNet50, Inceptionv3, Densenet201, Inceptionresnetv2, MobileNetv2, GoogleNet). The determination of hyperparameters of Machine Learning (ML) algorithms by Bayesian optimization, and ANN-based image segmentation are the two main contributions in this study. First of all, lung segmentation is performed automatically from the raw image with Artificial Neural Networks (ANNs). To ensure data diversity, data augmentation is applied to the COVID-19 classes, which are fewer than the other two classes. Then these images are applied as input to five different CNN models. The features extracted from each CNN model are given as input to four different ML algorithms, namely Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Naive Bayes (NB), and Decision Tree (DT) for classification. To achieve the most successful classification accuracy, the hyperparameters of each ML algorithm are determined using Bayesian optimization. With the classification made using these hyperparameters, the highest success is obtained as 96.29% with the DenseNet201 model and SVM algorithm. The Sensitivity, Precision, Specificity, MCC, and F1-Score metric values for this structure are 0.9642, 0.9642, 0.9812, 0.9641 and 0.9453, respectively. These results showed that ML methods with the most optimum hyperparameters can produce successful results.
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    A new method for skull stripping in brain MRI using multistable cellular neural networks
    (Springer London Ltd, 2018) Yilmaz, Burak; Durdu, Akif; Emlik, Ganime Dilek
    This study proposes a new method on detecting brain region in MRI data. This task is generally named as skull stripping in the literature. The algorithm is developed by using the cellular neural networks (CNNs) and multistable CNN structures. It also includes a contrast enhancement and noise reduction algorithm. The algorithm is named as multistable cellular neural network on MRI for skull stripping (mCNN-MRI-SS). Three different case studies are performed for measuring the success of the algorithm. Also a fourth case study is performed to evaluate the supporting algorithm, the CEULICA. First two evaluations are performed by using well-known MIDAS-NAMIC and Brainweb databases, which are properly organized Talairach-compatible databases. The third database was obtained from the research and application hospital of Necmettin Erbakan University Meram Faculty of Medicine. These MRI data were not Talairach-compatible and less sampled. The algorithm achieved 0.595 Jaccard, 0.744 Dice, 0.0344 TPF and 0.383 TNF mean values with the Brainweb T1-weighted images and 0.837 Jaccard, 0.898 Dice, 0.0124 TPF and 0.1511 TNF mean values with the MIDAS-NAMIC T2-weighted images. The algorithm achieved 0.8297 Jaccard, 0.9012 Dice, 0.0951 TPF and 0.1225 TNF mean values and achieved with the obtained data the best values among the other algorithms. As a result, it can be claimed that algorithm performs best with the non-Talairach-compatible MRI data due to its nature of performing at cellular level.
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    A Novel Convolutional-Recurrent Hybrid Network for Sunn Pest-Damaged Wheat Grain Detection
    (Springer, 2022) Sabanci, Kadir; Aslan, Muhammet Fatih; Ropelewska, Ewa; Unlersen, Muhammed Fahri; Durdu, Akif
    The sunn pest-damaged (SPD) wheat grains negatively affect the flour quality and cause yield loss. This study focuses on the detection of SPD wheat grains using deep learning. With the created image acquisition mechanism, healthy and SPD wheat grains are displayed. Image preprocessing steps are applied to the captured raw images, then data augmentation is performed. The augmented image data is given as an input to two different deep learning architectures. In the first architecture, transfer learning application is made using AlexNet. The second architecture is a hybrid structure, obtained by adding the bidirectional long short-term memory (BiLSTM) layer to the first architecture. In terms of accuracy, the performance of the non-hybrid and hybrid architectures that are presented in the study is determined as 98.50% and 99.50%, respectively. High classification success and innovative deep learning structure are the features of this study that distinguish it from previous studies.