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Automatic detection of Malaria infected RBCs from a focus stack of bright field microscope slide images

Published: 18 December 2016 Publication History

Abstract

Malaria is a deadly infectious disease affecting red blood cells in humans due to the protozoan of type Plasmodium. In 2015, there is an estimated death toll of 438, 000 patients out of the total 214 million malaria cases reported world-wide. Thus, building an accurate automatic system for detecting the malarial cases is beneficial and has huge medical value. This paper addresses the detection of Plasmodium Falciparum infected RBCs from Leishman's stained microscope slide images. Unlike the traditional way of examining a single focused image to detect the parasite, we make use of a focus stack of images collected using a bright field microscope. Rather than the conventional way of extracting the specific features we opt for using Convolutional Neural Network that can directly operate on images bypassing the need for hand-engineered features. We work with image patches at the suspected parasite location there by avoiding the need for cell segmentation. We experiment, report and compare the detection rate received when only a single focused image is used and when operated on the focus stack of images. Altogether the proposed novel approach results in highly accurate malaria detection.

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      cover image ACM Other conferences
      ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing
      December 2016
      743 pages
      ISBN:9781450347532
      DOI:10.1145/3009977
      © 2016 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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      • Google Inc.
      • QI: Qualcomm Inc.
      • Tata Consultancy Services
      • NVIDIA
      • MathWorks: The MathWorks, Inc.
      • Microsoft Research: Microsoft Research

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      New York, NY, United States

      Publication History

      Published: 18 December 2016

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      Author Tags

      1. CNN
      2. Malaria diagnosis
      3. plasmodium falciparum

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      ICVGIP '16
      Sponsor:
      • QI
      • MathWorks
      • Microsoft Research

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      ICVGIP '16 Paper Acceptance Rate 95 of 286 submissions, 33%;
      Overall Acceptance Rate 95 of 286 submissions, 33%

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      • (2024)A novel approach for identification of zoonotic trypanosome utilizing deep metric learning and vector database-based image retrieval systemHeliyon10.1016/j.heliyon.2024.e30643(e30643)Online publication date: May-2024
      • (2023)Blood Cell Counting and Malaria Pathogen Detection using Convolutional Neural Network2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)10.1109/ICESC57686.2023.10193462(1120-1127)Online publication date: 6-Jul-2023
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      • (2020)Malaria Detection on Giemsa-Stained Blood Smears Using Deep Learning and Feature ExtractionInternational Conference on Innovative Computing and Communications10.1007/978-981-15-1286-5_70(789-803)Online publication date: 29-Feb-2020
      • (2019)Deep Learning Based Classification of Malaria from Slide Images2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT)10.1109/EBBT.2019.8741702(1-4)Online publication date: Apr-2019

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