Benchmarking Classification Models for Cell Viability on Novel Cancer Image Datasets

Ozkan A., İŞGÖR S. B., ŞENGÜL G., İŞGÖR Y. G.

CURRENT BIOINFORMATICS, cilt.14, sa.2, ss.108-114, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 2
  • Basım Tarihi: 2019
  • Doi Numarası: 10.2174/1574893614666181120093740
  • Dergi Adı: CURRENT BIOINFORMATICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.108-114
  • Anahtar Kelimeler: Cell viability, pattern classification, computer vision, hemocytometer, cancer cells, HL60, K562, LOCAL PHASE QUANTIZATION, TEXTURE CLASSIFICATION, PREDICTION, LINE
  • Ankara Üniversitesi Adresli: Evet

Özet

Background: Dye-exclusion based cell viability analysis has been broadly used in cell biology including anticancer drug discovery studies. Viability analysis refers to the whole decision making process for the distinction of dead cells from live ones. Basically, cell culture samples are dyed with a special stain called trypan blue, so that the dead cells are selectively colored to darkish. This distinction provides critical information that may be used to expose influences of the studied drug on considering cell culture including cancer. Examiner's experience and tiredness substantially affect the consistency throughout the manual observation of cell viability. The unsteady results of cell viability may end up with biased experimental results accordingly. Therefore, a machine learning based automated decision-making procedure is inevitably needed to improve consistency of the cell viability analysis.