Browsing by Author "Engin M."

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    Comparative analysis of ANN performance of four feature extraction methods used in the detection of epileptic seizures
    (Elsevier Ltd, 2023) Acar Demirci B.; Demirci O.; Engin M.
    Epilepsy, a prevalent neurological disorder characterized by disrupted brain activity, affects over 70 million individuals worldwide, as reported by the World Health Organization (WHO). The development of computer-aided diagnosis systems has become vital in assessing epilepsy severity promptly and initiating timely treatment. These systems enable the detection of epileptic seizures by analyzing the electrical activity in the EEG recordings of the patients. In addition, it helps doctors to choose suitable treatment by quickly determining the type, duration, and characteristics of seizures and increases the patient's quality of life. The proposed computer-aided diagnosis system in this study comprises three modules: preprocessing, feature extraction, and classification. The initial module employs a low-pass Chebyshev II filter to eliminate noise artifacts from signal recordings. The second module involves deriving feature vectors using Bispectrum Analysis, Empirical Mode Decomposition, Discrete Wavelet Transform, and Wavelet Packet Analysis. The third module employs the Artificial Neural Networks method for epileptic seizure detection. This study not only enables the comparison of feature extraction efficacy among Bispectrum Analysis, Empirical Mode Decomposition, Discrete Wavelet Transform, and Wavelet Packet Analysis techniques, but it also reveals that Bispectrum Analysis and Empirical Mode Decomposition yield the highest accuracy rate. The method achieves 100% accuracy in detecting epileptic seizures. Additionally, sensitivity analysis has been conducted to enhance the success of Discrete Wavelet Transform and Wavelet Packet Analysis methods and to identify significant features. © 2023 Elsevier Ltd
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    In vitro analysis of breast tumour detection using rotational infrared thermal imaging and machine learning techniques
    (Taylor and Francis Ltd., 2025) Acar Demirci B.; Engin M.
    Breast cancer is the most common cancer affecting women worldwide, and various methods, such as biopsy, mammography, 3D tomosynthesis, MRI, ultrasonography, and infrared thermal imaging (ITI), are utilized for its early detection. ITI is a technique that detects variations in thermal patterns on the breast surface, which are caused by the higher metabolic activity and vascularisation of cancerous cells. I As a radiation-free, non-invasive, and cost-effective screening method, ITI has been studied using in-silico, in-vivo, and in-vitro approaches to enhance its diagnostic performance and develop reliable imaging algorithms. Conventional ITI in in-vivo studies is limited by fixed imaging positions, making it difficult to detect deep or hidden tumours. To address these limitations, this study introduces a rotational ITI method integrated with machine learning algorithms in an in-vitro environment. The proposed method generates datasets with varying tumour depths for comprehensive algorithmic analysis. It captures thermal images from four distinct positions (0°, 90°, 180°, and 270°), enabling a more thorough evaluation of the phantom breast surface. Using the combined dataset, which incorporates information from all four positions, the Convolutional Autoencoder and Support Vector Machines methods achieved an accuracy of 98.28%, sensitivity of 97.75%, specificity of 98.82%, and an F1 score of 98.29%. © 2025 Informa UK Limited, trading as Taylor & Francis Group.