Detection of Overlapping Cells in Histopathological Images with Deep Sparse Learning Derin Seyrek ?grenme ile Histopatolojik G r nt lerde rt sen H crelerin Tespiti

Akarsu E., BİLGİN G.

33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025, İstanbul, Türkiye, 25 - 28 Haziran 2025, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/siu66497.2025.11112015
  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: cell classification, deep learning, histopathology, medical image analysis, tissue segmentation
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

Nowadays, artificial intelligence is rapidly developing, and with increasing data volumes, the learning capacity of models is expanding. However, this also increases training costs and processing times. In this study, deep sparse learning (DSL) methods are evaluated for the classification of cancer cells and tissues. The Ocelot dataset was used to analyze cell and tissue images. While the highest F1 Score reported in the literature is 75.58%, the proposed method improved the initial F1 Score from 69.45% to 73.12%. Additionally, the DSL model, supported by data augmentation techniques, achieved a 20% improvement in processing time. The findings demonstrate that DSL not only improves accuracy but also reduces processing time, providing more efficient and cost-effective solutions in the field of medical image processing.