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    Deep Transfer Learning to Classify Mass and Calcification Pathologies from Screen Film Mammograms
    (2023) Tiryaki, Volkan Müjdat
    The number of breast cancer diagnoses is the largest among all cancers among women in the world. Breast cancer treatment is possible if it is diagnosed in the early stages. Mammography is a common imaging technique to detect breast cancer abnormalities. Breast cancer symptom screening is being performed by radiologists. In the last decade, deep learning was successfully applied to big image classification databases such as the ImageNet. In this study, the breast cancer pathology classification performances of the recent deep learning models were investigated by transfer learning and fine tuning. A total of 3,360 mammogram patches were used from the Digital Database for Screening Mammography (DDSM) and the Curated Breast Imaging Subset of DDSM (CBIS-DDSM) mammogram databases for deep learning model training, validating, and testing. Transfer learning and fine tuning were applied using Resnet50, Xception, NASNet, and EfficientNet-B7 network weights. The best classification performance was achieved by transfer learning from the Xception network. The computational costs of deep learning models were considered while selecting the best one. On the original CBIS-DDSM five-way test mammogram classification problem, the mean sensitivity, specificity, F1-score, and AUC were 0.7054, 0.9264, 0.7024, and 0.9317, respectively. The results show that the proposed models may be useful for the classification of breast cancer pathologies
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    Mamografi görüntülerindeki anormalliklerin yerel ikili örüntü ve varyantları kullanılarak sınıflandırılması
    (2020) Tiryaki, Volkan Müjdat
    Meme kanseri teşhisinde kullanılan mamografilerdeki anormalliklerin sınıflandırılması için makine öğrenmearaştırmaları büyük önem arz etmektedir. Bu çalışmada Curated Breast Imaging Subset of Digital Database forScreening Mammography (CBIS-DDSM) görüntü tabanındaki kitleli ve kalsifikasyonlu mamografi görüntülerisınıflandırılmıştır. Veri setindeki görüntülerden Yerel İkili Örüntü(YİÖ), Yerel Türev Örüntü, Yerel DörtlüÖrüntü(YDÖ), ve Gürültüye Dirençli Yerel İkili Örüntü yöntemleri ile doku öznitelikleri çıkarılmıştır. Öznitelikçıkarım yöntemlerinden yerel çarpıklık örüntü tabanlı ayrıntılı histogram yöntemiyle de öznitelik çıkarımıyapılmıştır. Daha sonra öznitelik vektörleri doğrusal ve radyal tabanlı fonksiyon kernel destek vektörmakineleri(DVM) ve yapay sinir ağları (YSA) kullanılarak sınıflandırılmıştır. Eğitim ve doğrulama verisi için 5-kez çapraz doğrulama yöntemi uygulanmıştır. En yüksek sınıflandırma performansı veren eşik seviyeleri vepencere boyutları her bir öznitelik çıkarım yöntemi için belirlenmiştir. Öznitelik çıkarımı için gerekli olan sürelertablo halinde verilmiştir. Öznitelik çıkarım yöntemi olarak farklı çap ve nokta sayısı ile hesaplanmış YİÖ vektörlerifüzyonu ve sınıflandırıcı olarak 2 gizli katmanlı YSA kullanılması durumunda test verisi için %85.74 başarı oranıelde edilmiştir. Elde edilen başarı oranları literatürdeki makine öğrenmesi sonuçlarına göre yüksek ve derinöğrenme sonuçları ile kıyaslanabilir sonuçlardır.
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    Management of wireless communication systems using artificial intelligence-based software defined radio
    (International Association of Online Engineering, 2020) Bargarai, Faiq A. Mohammed; Abdulazeez, Adnan Mohsin; Tiryaki, Volkan Müjdat; Zeebaree, Diyar Qader
    The wireless communication system was investigated by novel methods, which produce an optimized data link, especially the software-based methods. Software-Defined Radio (SDR) is a common method for developing and implementing wireless communication protocols. In this paper, SDR and artificial intelligence (AI) are used to design a self-management communication system with variable node locations. Three affected parameters for the wireless signal are considered: channel frequency, bandwidth, and modulation type. On one hand, SDR collects and analyzes the signal components while on the other hand, AI processes the situation in real-time sequence after detecting unwanted data during the monitoring stage. The decision was integrated into the system by AI with respect to the instantaneous data read then passed to the communication nodes to take its correct location. The connectivity ratio and coverage area are optimized nearly double by the proposed method, which means the variable node location, according to the peak time, increases the attached subscriber by a while ratio. © 2020 International Association of Online Engineering.
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    Texture-based segmentation and a new cell shape index for quantitative analysis of cell spreading in AFM images
    (Wiley-Liss Inc., 2015) Tiryaki, Volkan Müjdat; Adia-Nimuwa, Usienemnfon; Ayres, Virginia M.; Ahmed, Ijaz; Shreiber, David I.
    A new cell shape index is defined for use with atomic force microscopy height images of cell cultures. The new cell shape index reveals quantitative cell spreading information not included in a conventional cell shape index. A supervised learning-based cell segmentation algorithm was implemented by texture feature extraction and a multi-layer neural network classifier. The texture feature sets for four different culture surfaces were determined from the gray level co-occurrence matrix and local statistics texture models using two feature selection algorithms and by considering computational cost. The quantitative morphometry of quiescent-like and reactive-like cerebral cortical astrocytes cultured on four different culture environments was investigated using the new and conventional cell shape index. Inclusion of cell spreading with stellation information through use of the new cell shape index was shown to change biomedical conclusions derived from conventional cell shape analysis based on stellation alone. The new CSI results showed that the quantitative astrocyte spreading and stellation behavior was induced by both the underlying substrate and the immunoreactivity of the astrocytes. © 2015 International Society for Advancement of Cytometry.