NDLI: Optimised hybrid classifiers for automatic HEp-2 cell classification

Content Provider	IET Digital Library
Author	Chandran, Manju Chariyamparambil Jose, Marianthiran Victor
Abstract	This paper introduces a new HEp-2 cell classification model under two phases. In the initial phase, the input image is segmented using the morphological operations (opening and closing), and the segmented image is given for Convolutional Neural Network (CNN) classifier that gives the classified output. In the second phase, the given input is processed under (i) segmentation process (ii) Feature Extraction, and (iii) Classification. From the segmented images; the features like Gray level co-occurrence Matrix TGLCM) and Gray level Run Length Matrix (GLRM) are extracted. After extracting the features, they are subjected to a classification process, where Neural Network (NN) is used. Finally, the mean of both classified output (first phase and second phase) is considered to be the final classified output. As the main contribution, to enhance the classification accuracy, the hidden neurons of both classifiers (CNN and NN) are optimally chosen during the classification process. To make this possible, this paper aims to propose a new Randomized Update based Grey Wolf Optimization (RP-GWO) algorithm. Finally, the performance of the implemented approach is compared over other conventional approaches and its superiority is proven with respect to certain measures.
Starting Page	4316
Ending Page	4328
Page Count	13
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 16, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2020.0046
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/16
Journal	IET Image Processing
Publisher Date	2020-12-14
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automatic HEp-2 Cell Classification Biology And Medical Computing Cellular Biophysics Classification Accuracy Classification Process Co-occurrence Matrix Computer Vision And Image Processing Technique Computer-aided Diagnosis Convolutional Neural Network Classifier Diseases Feature Extraction Final Classified Output Grey-level Run-length Matrix HEp-2 Cell Classification Model Huge Intra-class Deviations Human Epithelial Type 2 Cell Image Classifications Image Classification Image Recognition Image Representation Image Segmentation Image Texture Improved Diagnosis Ability Input Image Knowledge Engineering Technique Medical Image Processing Neural Nets Optical, Image And Video Signal Processing Optimised Hybrid Classifier Patient Diagnostic Method And Instrumentation Randomised Update-based Grey Wolf Optimisation Algorithm Segmentation Process Segmented Image
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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

Classification of textured images based on new information fusion methods

Comparative analysis of texture feature extraction techniques for rice grain classification

Grey Wolf optimisation-based feature selection and classification for facial emotion recognition

Generalised deep learning framework for HEp-2 cell recognition using local binary pattern maps

Hybrid DSSCS and convolutional neural network for peripheral blood cell recognition system

Fully automatic figure-ground segmentation algorithm based on deep convolutional neural network and GrabCut

Tool for automatic tuning of binarisation techniques

Hierarchical content classification and script determination for automatic document image processing

Automatic segmentation and classification of lung tumour using advance sequential minimal optimisation techniques

Optimised hybrid classifiers for automatic HEp-2 cell classification

Similar Documents

Classification of textured images based on new information fusion methods

Comparative analysis of texture feature extraction techniques for rice grain classification

Grey Wolf optimisation-based feature selection and classification for facial emotion recognition

Generalised deep learning framework for HEp-2 cell recognition using local binary pattern maps

Hybrid DSSCS and convolutional neural network for peripheral blood cell recognition system

Fully automatic figure-ground segmentation algorithm based on deep convolutional neural network and GrabCut

Tool for automatic tuning of binarisation techniques

Hierarchical content classification and script determination for automatic document image processing

Automatic segmentation and classification of lung tumour using advance sequential minimal optimisation techniques

Optimised hybrid classifiers for automatic HEp-2 cell classification