U. Rajendra Acharya
Ngee Ann Polytechnic
730 Papers
1.7K Citations
U. Rajendra Acharya is an academic researcher from Ngee Ann Polytechnic. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 90, co-authored 570 publications. Previous affiliations of U. Rajendra Acharya include Kumamoto University & University of Southern Queensland.
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Papers
Automated Arrhythmia Detection Based on RR Intervals
Oliver Faust,Murtadha Kareem,Ali Ali,Edward J. Ciaccio,U. Rajendra Acharya +4 more
- 10 Aug 2021
TL;DR: In this article, a deep learning algorithm was proposed to discriminate AFIB, AFL, and NSR RR interval signals, which achieved the following results: ACC = 99.98%, SEN = 100.00%, and SPE = 0.94%.
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Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives.
U. Raghavendra,Anjan Gudigar,Aritra Paul,T. S. Goutham,Mahesh Anil Inamdar,Ajay Hegde,Aruna Devi,Chui Ping Ooi,Ravinesh C. Deo,Prabal Datta Barua,Filippo Molinari,Edward J. Ciaccio,U. Rajendra Acharya +12 more
TL;DR: In this article , the challenges faced by CAD systems based on different modalities are highlighted along with the current requirements of this domain and future prospects in this area of research, and a review of 124 research articles published from 2000 to 2022 is presented.
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Deep neural network technique for automated detection of ADHD and CD using ECG signal.
Hui Wen Loh,Chui Ping Ooi,Shu Lih Oh,Prabal Datta Barua,Yi Ren Tan,Filippo Molinari,Sonja March,U. Rajendra Acharya,Daniel Fung +8 more
TL;DR: The goal of this pilot study is to create the first explainable deep learning model for objective ECG-based ADHD/CD diagnosis as having an objective biomarker may improve diagnostic accuracy and allow biosignal-based computer-aided diagnostic (CAD) tools to be implemented in healthcare and ambulatory settings.
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Ensemble selection for feature-based classification of diabetic maculopathy images
Pradeep Chowriappa,Sumeet Dua,U. Rajendra Acharya,U. Rajendra Acharya,M. Muthu Rama Krishnan +4 more
TL;DR: The objective of the proposed decision system is three fold namely, to automatically extract textural features, to effectively choose subset of discriminatory features, and to classify DM fundus images to their corresponding class of disease severity.
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Application of intuitionistic fuzzy histon segmentation for the automated detection of optic disc in digital fundus images
Muthu Rama Krishnan M,U. Rajendra Acharya,Chua Kuang Chua,Lim Choo Min,Eddie Y. K. Ng,Milind M. Mushrif,Augustinus Laude +6 more
- 08 Jun 2012
TL;DR: A novel automated, reliable and efficient optic disc localization and segmentation method using digital fundus images is proposed, and shows the superiority of proposed fuzzy segmentation technique over other two segmentation methods.
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