Yazar "Al-Azzawi, Athar" seçeneğine göre listele

Listeleniyor 1 - 4 / 4
  • [ X ]
    Öğe
    Classification of the level of alzheimer’s disease using anatomical magnetic resonance images based on a novel deep learning structure
    (CRC Press, 2023) Al-Jumaili, Saif; Al-Azzawi, Athar; Uçan, Osman Nuri; Duru, Adil Deniz
    Alzheimer’s is an incurable neurodegenerative disease that generally begins slowly and progresses gradually with time. In the early stage, symptoms of memory loss are mild, while in the late stage it clearly shows the deterioration in cognitive functions. Due to its irreversible nature, early detection reflects positively on reducing restraining the spread and preventing damage to the brain cells thus avoiding reaching the dementia stage. Till now, deep learning is considered to be one of the most significant methodologies used to detect and classify different types of neurological diseases from MRI images. However, in this study, we proposed a novel two-dimensional deep convolutional neural network to classify four stages of Alzheimer’s disease. The dataset consists of four types, namely nondemented, very mild demented, mild demented, and moderate demented subject MR images. First, we have applied a preprocessing technique to resize the image for compliance with our models. Then, we performed Reduce Atmospheric Haze techniques that can decrease the atmospheric haze making all images sharp and clear to feed to the model. We implemented the model 30 times and obtained more than 99.46% for evaluation metrics. The proposed method shows an outstanding performance compared to other papers reported in the literature.
  • Küçük Resim
    Öğe
    Evaluation of deep transfer learning methodologies on the COVID-19 radiographic chest images
    (International Information and Engineering Technology Association, 2023) Al-Azzawi, Athar; Al-Jumaili, Saif; Duru, Adil Deniz; Duru, Dilek Göksel; Uçan, Osman Nuri
    In 2019, the world had been attacked with a severe situation by the new version of the SARSCOV- 2 virus, which is later called COVID-19. One can use artificial intelligence techniques to reduce time consumption and find safe solutions that have the ability to handle huge amounts of data. However, in this article, we investigated the classification performance of eight deep transfer learning methodologies involved (GoogleNet, AlexNet, VGG16, MobileNet-V2, ResNet50, DenseNet201, ResNet18, and Xception). For this purpose, we applied two types of radiographs (X-ray and CT scan) datasets with two different classes: non-COVID and COVID-19. The models are assessed by using seven types of evaluation metrics, including accuracy, sensitivity, specificity, negative predictive value (NPV), F1- score, and Matthew's correlation coefficient (MCC). The accuracy achieved by the X-ray was 99.3%, and the evaluation metrics that were measured above were (98.8%, 99.6%, 99.6%, 99.0%, 99.2%, and 98.5%), respectively. Meanwhile, the CT scan model classified the images without error. Our results showed a remarkable achievement compared with the most recent papers published in the literature. To conclude, throughout this study, it has been shown that the perfect classification of the radiographic lung images affected by COVID- 19.
  • [ X ]
    Öğe
    Fourier transform based epileptic seizure features classification using scalp electrical measurements using KNN and SVM
    (Altınbaş Üniversitesi, 2021) Al-Azzawi, Athar; Ibrahim, Abdullahi Abdu
    The great development that took place in the technology of the interaction between humans and computers led to remarkable and incredible success in many scientific fields. Until this day, researchers' studies are continuing in this field to reach the highest possible accuracy to obtain the results that approximate the accuracy of human work. Electroencephalogram (EEG) is one of the devices that took a wide space and led many studies to amazing results in the field of recording, analyzing, detecting, and classifying brain signals. Where this technology was able to monitor the disorders, which happened in the brain and provide the ability to study the state of health of the brain. In addition, machine learning had Various techniques that were successfully involved in the classification of EEG signals. Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) were our specialties here among the most suitable techniques for classifying EEG data. This thesis aims to build a system to assist clinicians on three levels, first assisting in patients' automatic monitoring which leads to a digital memory easy to memorize. Secondly, reducing the work time by Supporting the clinicians' decision which assists acceleration in getting the goals with the least number of errors. Finally, assisting in identifying the appropriate medication and medical care that patients may need. Thus, for achieving this purpose, the dataset used here was from Temple University Hospital Seizure Corpus (TUH) [1]. TUH is considered one of the largest open-source databases and is the most extensive [2-4]. Moreover, to ensure the best results preprocessing techniques were implemented to Dataset. There are many techniques for EEG signal processing that feed the classification models with the best data. Fast Fourier Transform (FFT) was studied as one of the types of feature extraction methods for processing EEG signals. Eventually, the results associated with classifying seizure types showed SVM got the best classification accuracy compared with KNN where the accuracy was 99.5 % and 99 %, respectively.
  • [ X ]
    Öğe
    Pseudopapilledema diagnosis based on a hybrid approach using deep transfer learning
    (Institute of Electrical and Electronics Engineers Inc., 2023) Al-Azzawi, Athar; Al-Jumaili, Saif; Duru, Adil Deniz; Bayat, Oğuz; Kurnaz, Sefer; Uçan, Osman Nuri
    This Papilledema is edema caused by elevated pressure inside the brain near the area that leads the optic nerve to reach the eye. If left untreated, this condition can cause severe difficulties, for instance, aberrant optical changes, reduced sharpness of vision, and irreversible blindness. At present, an approach based on image processing for determining the degree of papilledema from color fundus images was given utilizing transfer learning approaches. The used dataset here contains 295 papilledema images, 295 pseudopapilledema images, and 779 control images. For the image preparation, a segmentation optimizer was utilized. The performance of the transfer learning techniques GoogleNet, MobileNetV2, ResNet-18, and ResNet-50 was then compared. Furthermore, Sensitivity and specificity and constructed ROC curves were calculated. The ResNet-50 employing the optimizer ADAM method performed best in the testing, with 98% total accuracy. The findings of the studies demonstrated that a combination of segmentation, optimization models, and transfer learning techniques may be utilized to determine the severity of papilledema automatically. The total accuracy was higher when compared to other similar studies described in the literature.