NDLI: Segmentation techniques for early cancer detection in red blood cells with deep learning-based classifier

Content Provider	IET Digital Library
Author	Shemona, Jeya Sudharsan Chellappan, Agees Kumar
Abstract	Red Blood Corpuscles called Erythrocytes are the most important element in blood composition which is mainly responsible in all living cells. To detect the cancer cell various methods are employed. In this paper, proper identification of cancer cells from unaffected RBCs are detected. The proposed novel method called Online Region Based Segmentation (ORBS) method is done that is used to find the regions of corpuscles. By using properties, metric is formulated for determination of shape which is abnormal in blood cells. Overall accuracy of 96.9% is obtained using proposed ORBS method and deep learning classification (DLC) method has accuracy of 97.1% that helps to diagnose cancer cells using feature extraction process done automatically. Sensitivity, specificity and precision values of the proposed segmentation method is found to be 96.7%, 95.6% and 98.4% respectively. The computation time was found as 22 seconds. Closeness of Proposed method in relative to True Positive values at the ROC curve indicates the performance as higher. Comparative analysis is made with ResNet-50 based on the different testing and training data at rate of 90%−10%, 80%−20% and 70%−30% respectively, which proves the robustness of proposed research work. Experimental results prove proposed system effectiveness compared with other detection methods.
Starting Page	1726
Ending Page	1732
Page Count	7
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 9, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1067
Journal	IET Image Processing
Publisher Date	2020-03-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biology And Medical Computing Blood Blood Composition Blood Samples Cancer Cancer Cell Cellular Biophysics Computer Vision And Image Processing Technique Data Handling Technique Deep Learning Classification Method Early Cancer Detection Feature Extraction Feature Extraction Process Healthy RBCs Image Segmentation Imaging Tool Learning in AI Living Cell Medical Image Processing Neural Computing Technique Neural Nets Online Region-based Segmentation Method Optical, Image And Video Signal Processing ORBS Method Patient Diagnostic Method And Instrumentation Pattern Classification Receiver Operating Characteristic Curve Red Blood Cell Red Blood Corpuscles
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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