Yazar "Ercelik, Cetin" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim Yok
    Öğe
    Effects of Some Deep Learning Models with Power Law Transformation on Brain Tumor Classification
    (Institute of Electrical and Electronics Engineers Inc., 2024) Ercelik, Cetin; Hanbay, KazIm
    Brain tumors are benign or malignant tumors that occur as a result of abnormal growth of various cell groups within the brain. These tumors are often a serious threat to human health and are widely prevalent worldwide. Magnetic Resonance Imaging (MRI) is the most common technique in medical imaging for the detection of brain tumors. MRI enables detailed examination of the complex structure of the brain and facilitates the detection of tumors. However, due to the complexity of brain tissue, manual diagnoses may often be inaccurate and adversely affect patients' treatment processes. Therefore, technologies such as image processing and deep learning models have become increasingly popular in recent years. In this study, Power Law transformation is used to enhance Magnetic Resonance (MR) images. The transformed images are then trained with deep learning models. The four different deep learning models used in this study are VGG16, VGG19, ResNet50, and MobileNet. The aim of the study is to determine the best-performing deep learning model by comparing the results obtained from untreated and treated MR images. The results of all the training sessions showed that the Power Law transformation had a positive impact on all deep learning models, and the pair with the highest performance was determined to be Power Law + MobileNet. © 2024 IEEE.
  • Küçük Resim Yok
    Öğe
    Noise Reduced SAR Ship Database
    (Institute of Electrical and Electronics Engineers Inc., 2024) Hanbay, Kazim; Uzen, Huseyin; Ozdemir, Taha Burak; Ercelik, Cetin
    Synthetic aperture radar (SAR) images are used extensively in agricultural applications, coastal boundary detection and object recognition. This imaging technology provides desired results in many challenging applications due to its ability to provide images with appropriate resolution in harsh weather and climate conditions. In this study, an image database was created to detect ships from SAR images. The images were preprocessed in accordance with the literature and made suitable for ship detection methods. The noise in the images was reduced with a deep learning-based architecture. Using this database, image processing and machine learning methods were used to develop ship detection methods. © 2024 IEEE.