Premium
Convolutional neural network‐based malaria diagnosis from focus stack of blood smear images acquired using custom‐built slide scanner
Author(s) -
Gopakumar Gopalakrishna Pillai,
Swetha Murali,
Sai Siva Gorthi,
Sai Subrahmanyam Gorthi R. K
Publication year - 2018
Publication title -
journal of biophotonics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.877
H-Index - 66
eISSN - 1864-0648
pISSN - 1864-063X
DOI - 10.1002/jbio.201700003
Subject(s) - convolutional neural network , computer science , scanner , artificial intelligence , focus (optics) , segmentation , blood smear , stack (abstract data type) , computer vision , pattern recognition (psychology) , image segmentation , object detection , malaria , pathology , medicine , physics , optics , programming language
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.