An Improved Deep Convolutional Model for Segmentation of Nucleus and Cytoplasm from Pap Stained Cell Images
| Autor: | Rakhi Thampi, K Sabeena, C. Gopakumar |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Cervical cancer
business.industry Computer science Cell Early detection Pattern recognition 02 engineering and technology Image segmentation medicine.disease Cervical smears 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine medicine.anatomical_structure Sørensen–Dice coefficient Cytoplasm Dysplasia Cytology 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Segmentation Artificial intelligence business Nucleus |
| Zdroj: | 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). |
| DOI: | 10.1109/icaccs48705.2020.9074244 |
| Popis: | For the early detection of cervical dysplasia, automated cervical cell analysis system requires an accurate segmentation of nucleus and cytoplasm from cells. The segmentation of cellular materials from pap stained cytology image is open issue due to touching and crowded cells, presence of inflammatory cells, mucus and blood in the image. In this paper, for detecting and analyzing cell components from cervical smears, we developed a deep convolution framework using FC-Densenet56. Here images from Herlev dataset are trained and tested in deep architectures. A combination of FC-DenseNet56 and ResNet101 were used in proposed method to get an accurate result. For the comparison purpose, the results of proposed segmentation were evaluated with Precision and Dice coefficient, that achieves better results than the works reported in the literature. The performance parameters such as Precision and Dice coefficient is obtained greater than 90% and Recall and IoU got values greater than 85%. Besides cervical smear images, the proposed methodology can be adopted for segmentation of other cytology images. |
| Databáze: | OpenAIRE |
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