publications

2025

1. A Pixel-Based Anchor Approach for Nuclei Detection in Cervical Cytology Imaging

   Ciro Russo, Yusuf B. Tanriverdi, Alessandro Bria, and 1 more author

   In Pattern Recognition. ICPR 2024 International Workshops and Challenges, 2025

   Abs DOI

   Nuclei detection in cervical cytology images holds significant promise for enhancing the early identification of cervical cancer. In this study, we apply GravityNet, a one-stage end-to-end detector designed ad hoc for detecting small regions, like cervical cell nuclei. This approach is based on pixel-based anchors, which serve as reference points on the image pixel grid and dynamically move towards the suspicious regions by inspecting the surrounding space. We conducted experiments on the publicly available Cervix93 dataset to evaluate the effectiveness of our method. Despite challenges posed by variations in staining techniques and cell morphology, our results show promising performance compared to the current state-of-the-art methods.

2022

1. Toward Automatic Water Pollution Analysis: A Machine Learning Approach for Water-Quality Monitoring Through Pattern Classification of Water Crystallization

   Lavdie Rada, Yusuf Baran Tanrıverdi, Ömer Ekmel Kara, and 2 more authors

   In Intelligent Computing Systems, 2022

   Abs DOI

   Heavy metal contamination in drinking water and water resources is one of the problems generated by increasing water demand and growing industrialization. Heavy metals can be toxic to humans and other living beings when their intake surpasses a certain threshold. Generally, heavy metal contamination analysis of water resources requires qualified experts with specialized equipment. In this paper, we introduce a method for citizen-based water-quality monitoring through simple pattern classification of water crystallization using a smartphone and portable microscope. This work is a first step toward the development of a Water Expert System smartphone application that will provide the ability to analyze water resource contamination remotely by sending images to the database and receiving an automatic analysis of the sample via machine learning software. In this study, we show the ability of the method to detect Fe 2 mg/1 L, 5 mg/L,10 mg/L polluted distilled water compared with other heavy metals (Al, Pb) pollution. The experimental results show that the classification used method has an accuracy greater than 90%.