Deep CNN Frameworks for Comparison for Malaria Diagnosis

Priyadarshini Adyasha Pattanaik; Zelong Wang; Patrick Horain

doi:10.21427/t6bj-zr14

Deep CNN Frameworks for Comparison for Malaria Diagnosis

Priyadarshini Adyasha Pattanaik, Zelong Wang, Patrick Horain

Research output: Contribution to conference › Paper › peer-review

Abstract

Abstract We compare Deep Convolutional Neural Networks (DCNN) frameworks, namely AlexNet and VGGNet, for the classification of healthy and malaria-infected cells in large, grayscale, low quality and low resolution microscopic images, in the case only a small training set is available. Experimental results deliver promising results on the path to quick, automatic and precise classification in unstrained images.

Original language	English
DOIs	https://doi.org/10.21427/t6bj-zr14
Publication status	Published - 1 Jan 2019
Externally published	Yes
Event	IMVIP 2019: Irish Machine Vision & Image Processing - Technological University Dublin, Dublin, Ireland Duration: 28 Aug 2019 → 30 Aug 2019

Conference

Conference	IMVIP 2019: Irish Machine Vision & Image Processing
Country/Territory	Ireland
City	Dublin
Period	28/08/19 → 30/08/19

Keywords

Deep Convolutional Neural Networks
DCNN
AlexNet
VGGNet
classification
malaria-infected cells
microscopic images
small training set
automatic classification
precise classification
unstrained images

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.21427/t6bj-zr14Licence: CC BY-NC-SA

Cite this

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title = "Deep CNN Frameworks for Comparison for Malaria Diagnosis",

abstract = "Abstract We compare Deep Convolutional Neural Networks (DCNN) frameworks, namely AlexNet and VGGNet, for the classification of healthy and malaria-infected cells in large, grayscale, low quality and low resolution microscopic images, in the case only a small training set is available. Experimental results deliver promising results on the path to quick, automatic and precise classification in unstrained images.",

keywords = "Deep Convolutional Neural Networks, DCNN, AlexNet, VGGNet, classification, malaria-infected cells, microscopic images, small training set, automatic classification, precise classification, unstrained images",

author = "\{Adyasha Pattanaik\}, Priyadarshini and Zelong Wang and Patrick Horain",

year = "2019",

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doi = "10.21427/t6bj-zr14",

language = "English",

note = "IMVIP 2019: Irish Machine Vision \& Image Processing ; Conference date: 28-08-2019 Through 30-08-2019",

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T1 - Deep CNN Frameworks for Comparison for Malaria Diagnosis

AU - Adyasha Pattanaik, Priyadarshini

AU - Wang, Zelong

AU - Horain, Patrick

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Abstract We compare Deep Convolutional Neural Networks (DCNN) frameworks, namely AlexNet and VGGNet, for the classification of healthy and malaria-infected cells in large, grayscale, low quality and low resolution microscopic images, in the case only a small training set is available. Experimental results deliver promising results on the path to quick, automatic and precise classification in unstrained images.

AB - Abstract We compare Deep Convolutional Neural Networks (DCNN) frameworks, namely AlexNet and VGGNet, for the classification of healthy and malaria-infected cells in large, grayscale, low quality and low resolution microscopic images, in the case only a small training set is available. Experimental results deliver promising results on the path to quick, automatic and precise classification in unstrained images.

KW - Deep Convolutional Neural Networks

KW - DCNN

KW - AlexNet

KW - VGGNet

KW - classification

KW - malaria-infected cells

KW - microscopic images

KW - small training set

KW - automatic classification

KW - precise classification

KW - unstrained images

U2 - 10.21427/t6bj-zr14

DO - 10.21427/t6bj-zr14

M3 - Paper

T2 - IMVIP 2019: Irish Machine Vision & Image Processing

Y2 - 28 August 2019 through 30 August 2019

ER -

Deep CNN Frameworks for Comparison for Malaria Diagnosis

Abstract

Conference

Keywords

UN SDGs

Access to Document

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Cite this