Fishman, Dmytro, juhendajaZeynalli, Ali2023-06-142023-06-142023https://hdl.handle.net/10062/90598Cell classification is one of the most complex challenges in cellular research that has significant importance to personalised medicine, cancer diagnostics and disease prevention. The accurate classification of cells based on their unique characteristics provides valuable insights into a patient's health status and in guiding treatment decisions. Thanks to recent technological advancements, cellular research has experienced significant progress in the use of deep learning and has become a valuable tool for tackling complicated tasks such as cell classification. In this study, we explored the capability of state-of-the-art deep learning models such as ResNet, ViT and Swin Transformer to automatically classify brightfield and fluorescent microscopy images across single and multiple channels into four cell treatments: Palbociclib, MLN8237, AZD1152, and CYC116. The results have revealed that Swin Transformer surpasses the other models for cell treatment classification on multi-channel fluorescent and brightfield images, achieving the highest accuracy of 86% and 59%, correspondingly. However, the highest accuracy achieved on single-channel brightfield images was 61%, using the ResNet-50 model. The previous research has shown that combining multiple channels yields better performance which necessitates further investigation into the capacity of deep learning models for automating the cell treatment classification of single- and multi-channel brightfield microscopy images.engopenAccessAttribution-NonCommercial-NoDerivatives 4.0 Internationalmachine learningdeep learningneural networksimage classificationThesis