Programs
- M. Tech. in Automotive Engineering -
- Fellowship In Interventional Pulmonology and Pulmonary Critical care - Fellowship
Publication Type : Journal Article
Publisher : Wiley Online Library
Source : Journal of biophotonics 11 (3), e201700003, 2018.
Url : https://onlinelibrary.wiley.com/doi/abs/10.1002/jbio.201700003
Campus : Amritapuri
School : School of Engineering
Center : Research & Projects
Department : Computer Science
Verified : Yes
Year : 2018
Abstract : The present paper introduces a focus stacking-based approach for automated quantitative detection of Plasmodium falciparum malaria from blood smear. For the detection, a custom designed convolutional neural network (CNN) operating on focus stack of images is used. The cell counting problem is addressed as the segmentation problem and we propose a 2-level segmentation strategy. Use of CNN operating on focus stack for the detection of malaria is first of its kind, and it not only improved the detection accuracy (both in terms of sensitivity [97.06%] and specificity [98.50%]) but also favored the processing on cell patches and avoided the need for hand-engineered features. The slide images are acquired with a custom-built portable slide scanner made from low-cost, off-the-shelf components and is suitable for point-of-care diagnostics. The proposed approach of employing sophisticated algorithmic processing together with inexpensive instrumentation can potentially benefit clinicians to enable malaria diagnosis.
Cite this Research Publication : G Gopakumar, M Swetha, G Sai Siva, GRK Sai Subrahmanyam, "Convolutional neural network‐based malaria diagnosis from focus stack of blood smear images acquired using custom‐built slide scanner," Journal of biophotonics 11 (3), e201700003, 2018.