| Title |
Automatic cell image classification with convolutional neural networks |
| Authors |
김상희(Sang-Hee Kim) ; 이재형(Jae-hyung Lee) ; 최은영(Eun-Young Choi) ; 전성태(Sung-tae Jeon) ; 최민영(Min-young Choi) ; 조서현(Seo-hyun Jo) ; 최세운(Se-woon Choe) |
| DOI |
https://doi.org/10.5370/KIEE.2021.70.1.139 |
| Keywords |
Automatic classification; HeLa cell; CCD-986SK; OpenCV; Convolutional Neural Network; Deep learning |
| Abstract |
Recently, artificial intelligence can be used in various fields, especially for medical purposes. For example, it can help diagnose lung diseases and cancer accurately and quickly, thereby reducing the time and cost of medical treatment. In this study, image data were acquired using cultured cervical cancer cells and skin fibroblast cells. The acquired images were pre-processed using OpenCV and enabled the creation of input data optimized for training. In addition, an optimal deep learning algorithm was designed to classify cells by type using transfer learning methods. As a result, the CNN-based learning and automatic classification method proposed in this experiment showed a high accuracy of over 98% and is expected to be used for accurate diagnosis and treatment of diseases in the future. |