Computer Engineering / Bilgisayar Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/10

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Author: search.filters.author.Yiğit, Altuğ

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  • Book Part
    Citation - Scopus: 2
    Dementia Detection With Deep Networks Using Multi-Modal Image Data
    (CRC Press, 2023) Yiğit, Altuğ; Işık, Zerrin; Baştanlar, Yalın
    Neurodegenerative diseases give rise to irreversible neural damage in the brain. By the time it is diagnosed, the disease may have progressed. Although there is no complete treatment for many types of neurodegenerative diseases, by detecting the disease in its early stages, treatments can be applied to relieve some symptoms or prevent disease progression. Many invasive and non-invasive methods are employed for the diagnosis of dementia. Computer-assisted diagnostic systems make the diagnosis based on volumetric features (structural or functional) or some two-dimensional brain perspectives obtained from a single image modality. This chapter firstly introduces a broad review of multi-modal imaging approaches proposed for dementia diagnosis. Then it presents deep neural networks, which extract structural and functional features from multi-modal imaging data, are employed to diagnose Alzheimer’s and mild cognitive impairments. While MRI scans are safer than most types of scans and provide structural information about the human body, PET scans provide information about functional activities in the brain. Thus, the setup has been designed to make experiments using both MRI and FDG-PET scans. Performances of multi-modal models were compared with single-modal solutions. The multi-modal solution showed superiority over single-modals due to the advantage of focusing on assorted features. © 2023 selection and editorial matter, Jyotismita Chaki; individual chapters, the contributors.
  • Article
    Citation - WoS: 8
    Citation - Scopus: 9
    Dementia diagnosis by ensemble deep neural networks using FDG-PET scans
    (Springer, 2022) Yiğit, Altuğ; Baştanlar, Yalın; Işık, Zerrin
    Dementia is a type of brain disease that affects the mental abilities. Various studies utilize PET features or some two-dimensional brain perspectives to diagnose dementia. In this study, we have proposed an ensemble approach, which employs volumetric and axial perspective features for the diagnosis of Alzheimer’s disease and the patients with mild cognitive impairment. We have employed deep learning models and constructed two disparate networks. The first network evaluates volumetric features, and the second network assesses grid-based brain scan features. Decisions of these networks were combined by an adaptive majority voting algorithm to create an ensemble learner. In the evaluations, we compared ensemble networks with single ones as well as feature fusion networks to identify possible improvement; as a result, the ensemble method turned out to be promising for making a diagnostic decision. The proposed ensemble network achieved an average accuracy of 91.83% for the diagnosis of Alzheimer’s disease; to the best of our knowledge, it is the highest diagnosis performance in the literature.