Microscopic Blood Smear Segmentation and Classification Using Deep Contour Aware CNN and Extreme Machine Learning
- Resource Type
- Conference
- Authors
- Razzak, Muhammad Imran; Naz, Saeeda
- Source
- 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) CVPRW Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 IEEE Conference on. :801-807 Jul, 2017
- Subject
- Computing and Processing
Blood
Image segmentation
Feature extraction
Shape
Diseases
Image color analysis
Microscopy
RBC
Blood Sample Analysis
cell morphology
image analysis
ELM
KWFLICM
- Language
- ISSN
- 2160-7516
Recent advancement in genomics technologies has opened a new realm for early detection of diseases that shows potential to overcome the drawbacks of manual detection technologies. In this work, we have presented efficient contour aware segmentation approach based based on fully conventional network whereas for classification we have used extreme machine learning based on CNN features extracted from each segmented cell. We have evaluated system performance based on segmentation and classification on publicly available dataset. Experiment was conducted on 64000 blood cells and dataset is divided into 80% for training and 20% for testing. Segmentation results are compared with the manual segmentation and found that proposed approach provided with 98.12% and 98.16% for RBC and WBC respectively whereas classification accuracy is shown on publicly available dataset 94.71% and 98.68% for RBC & its abnormalities detection and WBC respectively.