Artifact Removal using Elliptic Filter and Classification using 1D-CNN for EEG signals

Nagabushanam, P.; George, S. Thomas; Davu, Praharsha; Bincy, P.; Naidu, M.S.R.; Radha, S.

doi:10.1109/icaccs48705.2020.9074287

Cited by 9 publications

(1 citation statement)

References 20 publications

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“…To overcome this situation, many research groups have developed machine learning, deep learning techniques to classify as covid or non-covid [8][9][10][11]. The machine-learning (ML) featurebased methods have a wide range of applications, including many biomedical signal and image processing applications such as arrhythmia classification, epileptic seizure, and cancer detection [12][13][14][15][16][17][18][19]. Radiologists can benefit from computer-aided diagnostic systems to improve diagnostic accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Machine learning Classification approach for detection of Covid 19 using CT images

Chickaramanna¹,

Veerabhadrappa

Tejas

et al. 2022

EMITTER Int'l J. of Engin. Technol.

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Coronavirus disease 2019 popularly known as COVID 19 was first found in Wuhan, China in December 2019. World Health Organization declared Covid 19 as a transmission disease. The symptoms were cough, loss of taste, fever, tiredness, respiratory problem. These symptoms were likely to show within 11 –14 days. The RT-PCR and rapid antigen biochemical tests were done for the detection of COVID 19. In addition to biochemical tests, X-Ray and Computed Tomography (CT) images are used for the minute details of the severity of the disease. To enhance efficiency and accuracy of analysis/detection of COVID images and to reduce of doctors' time for analysis could be addressed through Artificial Intelligence. The dataset from Kaggle was utilized to analyze. The statistical and GLCM features were extracted from CT images for the classification of COVID and NON-COVID instances in this study. CT images were used to extract statistical and GLCM features for categorization. In the proposed/prototype model, we achieved the classification accuracy of 91%, and 94.5% using SVM and Random Forest respectively.

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