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Browsing by Author "Aykat, S."

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    Brain Tumor Detection From Brain Mri Images With Deep Learning Methods;
    (Institute of Electrical and Electronics Engineers Inc., 2024) Aykat, S.; Aykat, Şükrü
    In this research, a deep learning model is proposed for brain tumor detection using brain MRI image collection. Three pre-trained convolutional neural networks are used as feature extractors. The obtained features are classified as brain tumors, normal, and tumorous using four different classifiers. Our proposed model has achieved a remarkable accuracy of 99.58% in its analysis, which is better than standard techniques. In addition, the proposed method has shown better performance than the convolutional neural network models used in the analysis. © 2024 IEEE.
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    A Review Of Cuda Based Face Detection And Recognition Applications;
    (Institute of Electrical and Electronics Engineers Inc., 2019) Aykat, S.; Sertbas, A.; Aykat, Şükrü
    Face recognition is an important biometry used in many areas such as building security systems, biometric passports and identification, surveillance systems. Systems used in these areas need to be fast. In recent years, many applications have been developed by taking advantage of the GPU's parallel processing. In this study, face detection and face recognition studies with CUDA supported by GPGPU, which is a parallel computing platform and programming module developed by NVIDIA, are examined. To date, the literature on face detection and recognition studies with CUDA has been conducted. As a result of the study, it was observed that the face detection and face recognition operations performed by using the parallel processing power of CUDA can be performed much faster. Furthermore, it was concluded that if deep learning is used in CUDA based face recognition applications, face recognition operations will be performed in much shorter periods. © 2019 IEEE.