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 ASD detection using hybrid deep lightweight features extracted from EEG signals.
Mehmet Baygin,Sengul Dogan,Turker Tuncer,Prabal Datta Barua,Oliver Faust,N. Arunkumar,Enas Abdulhay,Elizabeth E. Palmer,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +10 more
TL;DR: The results strongly indicate that the proposed hybrid deep lightweight feature extractor is suitable for autism detection using EEG signals and is ready to serve as part of an adjunct tool that aids neurologists during autism diagnosis in medical centers.
118
Automated oral cancer identification using histopathological images: a hybrid feature extraction paradigm.
M. Muthu Rama Krishnan,Vikram Venkatraghavan,U. Rajendra Acharya,Mousumi Pal,Ranjan Rashmi Paul,Lim Choo Min,Ajoy Kumar Ray,Jyotirmoy Chatterjee,Chandan Chakraborty +8 more
TL;DR: A novel integrated index called Oral Malignancy Index (OMI) is proposed using the HOS, LBP, LTE features, to diagnose benign or malignant tissues using just one number to help the oral onco-pathologists to screen the subjects rapidly.
118
Explainable detection of myocardial infarction using deep learning models with Grad-CAM technique on ECG signals
TL;DR: In this paper , the authors developed DenseNet and CNN models for the classification of healthy subjects and patients with ten classes of MI based on the location of myocardial involvement, which achieved high classification accuracies.
118
Automated characterization of fatty liver disease and cirrhosis using curvelet transform and entropy features extracted from ultrasound images
U. Rajendra Acharya,U. Raghavendra,Hamido Fujita,Yuki Hagiwara,Joel Ew Koh,Tan Jen Hong,Vidya K. Sudarshan,Anushya Vijayananthan,Chai Hong Yeong,Anjan Gudigar,Kwan Hoong Ng +10 more
TL;DR: This work is proposing an algorithm to discriminate automatically the normal, FLD and cirrhosis ultrasound images using curvelet transform (CT) method and develops a liver disease index (LDI), which can significantly help the radiologists to discriminateFLD and Cirrhosis in their routine liver screening.
117
Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records.
Ozal Yildirim,Muhammed Talo,Edward J. Ciaccio,Ru San Tan,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +6 more
TL;DR: An effective deep neural network (DNN) model is proposed to detect different rhythm classes from a new ECG database comprising more than 10,000 records for automated detection of arrhythmia.
117