Sparse noise minimization in image classification using Genetic Algorithm and DenseNet

Mienye, ID, Kenneth Ainah, P, Emmanuel, ID ORCID: https://orcid.org/0000-0003-0695-8378 and Esenogho, E 2021, Sparse noise minimization in image classification using Genetic Algorithm and DenseNet , in: 2021 Conference on Information Communications Technology and Society (ICTAS), 10th-11th March 2021, Durban, South Africa.

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Abstract

Noise handling is a critical aspect of image processing, which can significantly affect the accuracy of classification and recognition algorithms. In this paper, we propose a technique for improved noise handling in sparse input feature maps where the noise signal is also sparse. The signal-noise relationship is formulated as an optimization problem which is solved by a genetic algorithm. The genetic algorithm is applied to optimize the setting of a non-convexity parameter which yields a more accurate image sparse matrix. The resulting feature map is then classified using a densely connected convolutional network (DenseNet). Lung computed tomography images were used for the experiments. The proposed approach achieves better performance when the classification results are compared with a case in which the input signal has not been denoised using the proposed approach.

Item Type: Conference or Workshop Item (Paper)
Additional Information: ** From Crossref proceedings articles via Jisc Publications Router **History: published 03-2021
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: 2021 Conference on Information Communications Technology and Society (ICTAS)
Publisher: IEEE
ISBN: 9781728180816 (online); 9781728180823 (print on demand)
Related URLs:
SWORD Depositor: Publications Router
Depositing User: Publications Router
Date Deposited: 23 Apr 2021 07:39
Last Modified: 23 Apr 2021 08:01
URI: http://usir.salford.ac.uk/id/eprint/60075

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