Convolution Neural Network Approaches for Cancer Cell Image Classification

• Published in: Biotechnology and bioprocess engineering Vol. 28; no. 5; pp. 707 - 719
• Main Authors: Kim, Chaeyoung, Shin, Sungtae, Jeong, Sehoon
• Format: Journal Article
• Language: English
• Published: Seoul The Korean Society for Biotechnology and Bioengineering 01.10.2023; Springer Nature B.V; 한국생물공학회
• Subjects: Artificial neural networks; Biotechnology; Breast cancer; breast neoplasms; Cancer; Cervical cancer; Chemistry; Chemistry and Materials Science; Classification; Colon cancer; Colorectal cancer; colorectal neoplasms; data collection; Deep learning; image analysis; Image classification; Industrial and Production Engineering; Lung cancer; lung neoplasms; Machine learning; Medical imaging; Medical research; neoplasm cells; Neural networks; Review Paper; Transfer learning; uterine cervical neoplasms; 생물공학; deep learning; binary class classification; cancer cell classification; convolutional neural network; multi-class classification

Recently, research incorporating the benefits of deep learning in the application of cancer cell classification and analysis has been actively conducted. In this paper, we investigated examples of binary-class classification and multi-class classification of cancer cell image data of commonly occurr...