Bilgisayar Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12514/4187

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Can deep learning replace histopathological examinations in the differential diagnosis of cervical lymphadenopathy?
(Springer, 2024) Can, Sermin; Türk, Ömer; Ayral, Muhammed; Kozan, Günay; Arı, Hamza; Akdağ, Mehmet; Yıldırım Baylan, Müzeyyen; Türk, Ömer
Introduction: We aimed to develop a diagnostic deep learning model using contrast-enhanced CT images and to investigate whether cervical lymphadenopathies can be diagnosed with these deep learning methods without radiologist interpretations and histopathological examinations. Material method: A total of 400 patients who underwent surgery for lymphadenopathy in the neck between 2010 and 2022 were retrospectively analyzed. They were examined in four groups of 100 patients: the granulomatous diseases group, the lymphoma group, the squamous cell tumor group, and the reactive hyperplasia group. The diagnoses of the patients were confirmed histopathologically. Two CT images from all the patients in each group were used in the study. The CT images were classified using ResNet50, NASNetMobile, and DenseNet121 architecture input. Results: The classification accuracies obtained with ResNet50, DenseNet121, and NASNetMobile were 92.5%, 90.62, and 87.5, respectively. Conclusion: Deep learning is a useful diagnostic tool in diagnosing cervical lymphadenopathy. In the near future, many diseases could be diagnosed with deep learning models without radiologist interpretations and invasive examinations such as histopathological examinations. However, further studies with much larger case series are needed to develop accurate deep-learning models.
Diagnosis of schizophrenia based on transformation from EEG sub-bands to the image with deep learning architecture
(Springer Science and Business Media Deutschland GmbH, 2023) Türk, Ömer; Aldemir, Erdoğan; Acar, Emrullah; Ertuğrul, Ömer Faruk; Türk, Ömer
Electroencephalogram is a low-cost, non-invasive, and high-entropy signal and thus has huge potential for clinical diagnosis of neurological diseases and brain–computer interface applications. Schizophrenia is one of the most severe diseases that show behavioral manifestations that are easily uncovered by specialists. In this context, the electroencephalogram analysis becomes more important for the automatic diagnosis of schizophrenia disease in the clinical process. In this study, a deep learning architecture, namely ResNet, aims to classify schizophrenia is proposed. The proposed system transforms wavelet sub-bands of the electroencephalogram into two-dimensional image space, which is considered the main unique contribution of the study. Thus, the disease indicators and features included in images could be figured out. Moreover, a discussion on the class activation maps was made to give a wide perspective on the features related to the disease. The proposed system was implemented on a large-scale electroencephalogram database containing records from unhealthy and healthy patients in various phases. The ResNet was implemented in three modes to give a thorough perspective in terms of the metrics of the diagnosis accuracy. The proposed system achieves 92.94% diagnosis accuracy rate, and the result shows that the proposed transformation-based solution is owing to the features related to schizophrenia disease

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