NDLI: An image processing based method to identify and grade conjunctivitis infected eye according to its types and intensity

Content Provider	IEEE Xplore Digital Library
Author	Tamuli, J. Jain, A. Dhan, A.V. Bhan, A. Dutta, M.K.
Copyright Year	2015
Description	Author affiliation: Amity Sch. of Eng. & Technol., Amity Univ., Noida, India (Tamuli, J.; Jain, A.; Dhan, A.V.; Bhan, A.; Dutta, M.K.)
Abstract	Inflammation of the conjunctiva and pain and discomfort in the inner surface of the eyelids is referred to as Conjunctivitis. It causes severe pain, burning sensation or in extreme cases blindness of the eye. Normally conjunctivitis is detected by eye specialist doctors and their limited number makes it difficult for everyone to reach them and get themselves diagnosed. This paper describes an automatic efficient image processing based method to identify conjunctivitis infected eye from a normal eye and classify it according to its types. Some statistical and texture features were used and then followed by PCA for extraction of discriminatory features and then classified using supervised learning method such as multi-class SVM and KNN. The intensity of the infected eyes were also calculated using the significant red plane. Plotconfusion was used to calculate the accuracy and a high accuracy was achieved using this method. Also in addition this proposed method is efficient, computationally fast and costs very low.
Starting Page	88
Ending Page	92
File Size	871027
Page Count	5
File Format	PDF
ISBN	9781467379472
e-ISBN	9781467379489
DOI	10.1109/IC3.2015.7346658
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-20
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Histogram Medical imaging Multi-class SVM Intensity Entropy KNN Classification algorithms Conjunctivitis GLCM PCA Support vector machines Microorganisms Feature extraction Principal component analysis
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Multi-class SVM classification combined with kernel PCA feature extraction of ECG signals

Efficient Gender Classification Using Interlaced Derivative Pattern and Principal Component Analysis

PCA and KPCA of ECG signals with binary SVM classification

GLCM texture classification for EEG spectrogram image

Bell pepper ripeness classification based on support vector machine

Performance analysis of multiclass object detection using SVM classifier

Fault Condition Recognition of mine hoist Combining Kernel PCA and SVM

Bearing Fault Diagnosis Based on PCA and SVM

Linear features, principal component analysis, and support vector machine for epileptic seizure prediction progress

An image processing based method to identify and grade conjunctivitis infected eye according to its types and intensity

Similar Documents

Multi-class SVM classification combined with kernel PCA feature extraction of ECG signals

Efficient Gender Classification Using Interlaced Derivative Pattern and Principal Component Analysis

PCA and KPCA of ECG signals with binary SVM classification

GLCM texture classification for EEG spectrogram image

Bell pepper ripeness classification based on support vector machine

Performance analysis of multiclass object detection using SVM classifier

Fault Condition Recognition of mine hoist Combining Kernel PCA and SVM

Bearing Fault Diagnosis Based on PCA and SVM

Linear features, principal component analysis, and support vector machine for epileptic seizure prediction progress

An image processing based method to identify and grade conjunctivitis infected eye according to its types and intensity