Darıcı, Muazzez Buket
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Name Variants
Muazzez Buket, Darici
D., Muazzez Buket
Darıcı,M.B.
D.,Muazzez Buket
Darıcı, M. B.
Darıcı, M.
Darıcı, Muazzez Buket
DARICI, MUAZZEZ BUKET
Darici,M.B.
Darici,Muazzez Buket
M. B. Darıcı
Darici, Muazzez Buket
Darıcı, MUAZZEZ BUKET
DARICI, Muazzez Buket
M. Darıcı
Muazzez Buket DARICI
Muazzez Buket Darıcı
MUAZZEZ BUKET DARICI
D., Muazzez Buket
Darıcı,M.B.
D.,Muazzez Buket
Darıcı, M. B.
Darıcı, M.
Darıcı, Muazzez Buket
DARICI, MUAZZEZ BUKET
Darici,M.B.
Darici,Muazzez Buket
M. B. Darıcı
Darici, Muazzez Buket
Darıcı, MUAZZEZ BUKET
DARICI, Muazzez Buket
M. Darıcı
Muazzez Buket DARICI
Muazzez Buket Darıcı
MUAZZEZ BUKET DARICI
Job Title
Araş. Gör.
Email Address
buket.darici@khas.edu.tr
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Scholarly Output
6
Articles
2
Citation Count
6
Supervised Theses
0
6 results
Scholarly Output Search Results
Now showing 1 - 6 of 6
Conference Object Citation Count: 1Improving Diabetic Retinopathy Detection Using Patchwise CNN with biGRU Model(Institute of Electrical and Electronics Engineers Inc., 2023) Darıcı, Muazzez Buket; Yiğit, GülsümThis study addresses Diabetic Retinopathy (DR), a diabetes complication that can lead to vision loss if not promptly diagnosed and treated. Recent advances in deep learning have shown promising results in detecting DR from retinal images. The study introduces a novel patch-based CNN-biGRU model for DR detection. The proposed model extracts patches from retinal images employing a sliding window strategy and then uses a Convolutional Neural Network (CNN) architecture to extract features from each patch. The features extracted from each patch are then concatenated, and a 4-layer bidirectional Gated Recurrent Unit (biGRU) is applied to predict the whole image. We assessed the proposed model on a publicly available dataset named APTOS 2019 Blindness Detection and achieved an accuracy of 73.5%, outperforming existing state-of-the-art approaches. The given patch-based CNN model can improve the accuracy of DR detection and aims to assist ophthalmologists in making more accurate diagnoses. © 2023 IEEE.Article Citation Count: 5Performance Analysis of Combination of CNN-based Models with Adaboost Algorithm to Diagnose Covid-19 Disease(Gazi Univ, 2023) Darıcı, Muazzez BuketAt the end of 2019, Covid-19, which is a new form of Coronavirus, has spread widely all over the world. With the increasing daily cases of this disease, fast, reliable, and automatic detection systems have been more crucial. Therefore, this study proposes a new technique that combines the machine learning algorithm of Adaboost with Convolutional Neural Networks (CNN) to classify Chest X-Ray images. Basic CNN algorithm and pretrained ResNet-152 have been used separately to obtain features of the Adaboost algorithm from Chest X-Ray images. Several learning rates and the number of estimators have been used to compare these two different feature extraction methods on the Adaboost algorithm. These techniques have been applied to the dataset, which contains Chest X-Ray images labeled as Normal, Viral Pneumonia, and Covid-19. Since the used dataset is unbalanced between classes SMOTE method has been used to make the number of images of classes balance. This study shows that proposed CNN as a feature extractor on the Adaboost algorithm(learning rate of 0.1 and 25 estimators) provides higher classification performance with 94.5% accuracy, 93% precision, 94% recall, and 93% F1-score.Article Citation Count: 0Brain Age Estimation from MRI Images using 2D-CNN instead of 3D-CNN(2021) Darıcı, Muazzez Buket; Yıldırım, Şüheda; Darici, Muazzez BuketHuman Brain Age has become a popular aging biomarker and is used to detect differences among healthy individuals. Because of the specific changes in the human brain with aging, it is possible to estimate patients’ brain ages from their brain images. Due to developments of the ability of CNN in classification and regression from images, in this study, one of the most popular state of the art models, the DenseNet model, is utilized to estimate human brain ages using transfer learning. Since this process requires high memory load with 3D-CNN, 2D-CNN is preferred for the task of Brain Age Estimation (BAE). In this study, some experiments are carried out to reduce the number of computations while preserving the total performance. With this aim, center slices of each three brain planes are used as the inputs of the DenseNet model, and different optimizers such as Adam, Adamax and Adagrad are used for each model. The dataset is selected from the IXI (Information Extraction from Images) MRI data repository. The MAE evaluation metric is used for each model with different input set to evaluate performance. The best achieved Mean Absolute Error (MAE) is 6.3 with the input set which consisted of center slices of the sagittal plane of brain scan and the Adamax parameter.Conference Object Citation Count: 0Sickle Cell Anemia Detection(IEEE, 2018) Darıcı, Muazzez Buket; Darıcı, Muazzez Buket; Kiracı, Furkan; Öğrenci, Arif Selçuk; Özmen, Atilla; Ertez, KeremAnemia is a common name given to falls in oxygen transport capacity due to some of the functional disadvantages of red blood cells. Pathology Laboratorians put the tissue on the microscope glass and try to diagnose Anemia disease. Processes have been taken for a long time and it has been caused to distract. Therefore it has been caused to misdiagnose the Laboratorian. This work shortens the diagnostic period of the disease and to minimizes error probability of this diagnosis by extracting healthy cells and just having sickle cells on the blood tissue using Image Processing Algorithms with an accuracy of 91.11 % precision of 92.9 % recall of 79.05 % for Sickle Cell Anemia.Conference Object Citation Count: 0A Siamese Network-Based Approach for Autism Spectrum Disorder Detection with Dual Architecture(Institute of Electrical and Electronics Engineers Inc., 2023) Yiğit, Gülsüm; Darıcı, Muazzez BuketAutism Spectrum Disorder (ASD) is a sophisticated neuro-developmental condition impacting numerous children. Early detection of ASD is crucial to implement suitable treatments to improve the daily activities of people with ASD. This paper introduces a system for ASD detection using facial images. The proposed model presents a unique system inspired by Siamese networks. Unlike traditional Siamese networks focusing on input pairs, our model leverages architectural pairs for feature combinations. During training, we combine features learned from different or the same architectures. This enables information transfer and improves the model's capture of comprehensive patterns. Experimental results on the 2940 facial images dataset demonstrate the effectiveness of our system, which exhibits improved accuracy compared to using individual architectures. When (ResNet50, VGG16) architecture pairs are employed in the proposed approach, the highest performance is obtained with an accuracy of 78.57%. Leveraging the strengths of multiple architectures, our model provides a comprehensive and robust representation of input data, leading to improved performance. © 2023 IEEE.Conference Object Citation Count: 0Comprehensive Analysis of Image Registration Techniques on Brain MR Images(Institute of Electrical and Electronics Engineers Inc., 2023) Darıcı, Muazzez Buket; Ozmen,A.Medical image registration is an important preprocess of image-guided systems. Since image registration brings the images to the same coordinate system of the specified reference image, image registration should not be neglected to be able to make accurate comparisons between results obtained from medical images. Basically, registration is an optimization problem. The parameters of the specified transformation algorithm are optimized based on specified functions and parameters of registration. In this study, T1-weighted structural 3D brain MR images on IXI dataset have been registered into reference image by the affine transformation in the proposed registration method. During experiments, the effects of several parameters and functions on registration performance have been investigated with different preprocessing techniques applied to brain MR images. After several experiments, the most successful outcome of various experiments was achieved by using Powell optimization function along with Linear Interpolation, when applying Median Filter with CLAHE to images in the suggested registration method. The NCC was used to compare the registration results. The study's results demonstrate that the proposed registration method outperformed the widely-used registration tool SPM8 with mean NCC of -0.753. © 2023 IEEE.
