Deep learning applications in digital and biomedical image processing

• Cell segmentation
• Computer-aided breast cancer
• Histopathologic image recognition and classification
• Gait recognition

• Siirt Üniversitesi Scientific Research Projects Directorate, 2021-SİÜMÜH-01 Deep learning based breast cancer diagnosis

• V. İMAÇ (Master of Science), Age estimation from gait data using machine learning, 2021.
• E. E. ETİZ (Master of Science), Deep learning based mass segmentation from digital mammograms, 2021.

• Tiryaki VM, Ayres VM, Ahmed I, Shreiber DI. Sub-micro scale cell segmentation using deep learning. Cytometry A. 2022 Jan 9. doi: 10.1002/cyto.a.24533.
• Tiryaki, V. M. (2020). Mamografi görüntülerindeki anormalliklerin yerel ikili örüntü ve varyantları kullanılarak sınıflandırılması . Bitlis Eren Üniversitesi Fen Bilimleri Dergisi , 9 (1) , 297-305 . DOI: 10.17798/bitlisfen.557411
• Hassan O. M. S., Abdulazeez A. M., Tiryaki V. M. Gait-Based Human Gender Classification Using Lifting 5/3 Wavelet and Principal Component Analysis, 2018 International Conference on Advanced Science and Engineering, DOI: 10.1109/ICOASE.2018.8548909
• V. M. Tiryaki, V. M. Ayres, I. Ahmed & D. I. Shreiber, A novel quantitative volumetric spreading index definition and assessment of astrocyte spreading in vitro, CYTOMETRY PART A, 2017, 1552-4922, 91A, 8, 794-799.
• V. M. Tiryaki, U. Adia-Nimuwa, V. M. Ayres, I. Ahmed; D. I. Shreiber, Texture-Based Segmentation and a New Cell Shape Index for Quantitative Analysis of Cell Spreading in AFM Images, CYTOMETRY PART A, 2015, 1552-4922, 87A, 12, 1090-1100.