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    A CNN-Based Novel Approach for Classification of Sacral Hiatus with GAN- Powered Tabular Data Set
    (Kaunas Univ Technology, 2023) Kilic, Ferhat; Korkmaz, Murat; Er, Orhan; Altin, Cemil
    epidural anaesthesia is usually the most well-known technique in obstetrics to deal with chronic back pain. Due to variations in the shape and size of the sacral hiatus (SH), its classification is a crucial and challenging task. Clinically, it is required in trauma, where surgeons must make fast and correct selections. Past studies have focused on morphometric and statistical analysis to classify it. Therefore, it is vital to automatically and accurately classify SH types through deep learning methods. To this end, we proposed the Multi-Task Process (MTP), a novel classification approach to classify the SH MTP that initially uses a small medical tabular data set obtained by manual feature extraction on computed tomography scans of the sacrums. Second, it augments the data set synthetically through a Generative Adversarial Network (GAN). In addition, it adapts a two-dimensional (2D) embedding algorithm to convert tabular features into images. Finally, it feeds images into Convolutional Neural Networks (CNNs). The application of MTP to six CNN models achieved remarkable classification success rates of approximately 90 % to 93 %. The proposed MTP approach eliminates the small medical tabular data problem that results in bone classification on deep models.
  • Küçük Resim
    Öğe
    EEG based cigarette addiction detection with deep learning
    (Int Information & Engineering Technology Assoc, 2024) Cay, Talip; Ölmez, Emre; Altin, Cemil; Tanik, Nermin
    In this study, cigarette addiction detection was performed using machine learning techniques with time -frequency feature extraction methods on EEG data collected from 30 different male individuals. Electroencephalography (EEG) data collected from individuals who underwent the Fagerstr & ouml;m Test for Nicotine Dependence (FTND) were labeled as dependent or non-dependent based on their test results. The obtained EEG data were first subjected to Discrete Wavelet Transform (DWT). Then, Power Spectral Density (PSD) analysis and feature extraction processes were performed separately on the outputs obtained from the DWT process. The data obtained from PSD analysis and feature extraction processes were classified using Artificial Neural Networks (ANN). The aim of this study is to achieve higher success rates in cigarette addiction detection by classifying EEG data with machine learning methods after extracting time -frequency features, rather than using traditional methods. In this study, responses to cigarette stimuli were classified using machine learning methods based on EEG graphs. The results revealed that temporal and prefrontal lobes were more distinctive in responses to cigarette stimuli, and success rates were higher in the theta frequency band.