EDITORIAL


Applications of Soft Computing and Machine Learning Techniques for Biomedical Signals and Images



Rahul K. Kher1, *, Chirag Paunwala2, Falgun Thakkar1, Heena Kher3, Mita Paunwala4
1 G. H. Patel College of Engineering & Technology, Gujarat 388120, India
2 Sarvajanik College of Engineering & Technology, Gujarat 395001, India
3 A. D. Patel Institute of Technology, Gujarat 388121, India
4 C. K. Pithawala College of Engineering & Technology, Gujarat 395007, India

Article Information


Identifiers and Pagination:

Year: 2021
Volume: 15
Issue: Suppl-2, M1
First Page: 131

Publisher ID: TOBEJ-15-131
DOI: 10.2174/1874120702115010131

Article History:

Electronic publication date: 31/12/2021
Collection year: 2021

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Creative Commons License
© 2021 Kher et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the G. H. Patel College of Engineering & Technology, India; E-mail: rahul2777@gmail.com


Biomedical signals like ECG, EEG, EMG, EOG, ERG, etc., and images such as ultrasound, MRI, CT, PET, etc., are very useful for assessing the wellbeing of a human being. In order to determine the abnormality in a particular organ or part of the body, physicians use these signals and images. Although today’s signal recorders and image scanners are of excellent resolution and quality, sometimes they fail to convey the actual scenario of the body part/organ [1-5].

Soft computing and machine learning methods play an important role in dealing with biomedical signals/images, and they have numerous applications like noise/artifact removal from signals/images, early detection of seizure/ cancer/tumors, the fusion of images for better diagnosis, classification of signals/images and many more.

This special issue aims to compile the novel research outcomes of various soft computing and machine learning algorithms applied to a variety of biomedical signals/images. We have received exceptional responses to this thematic issue. Some of the notable contributions include applications of deep learning and convolutional neural networks for cancer detection, content-based medical image retrieval, tumor detection using MRI, COVID-19 screening using chest radiography, machine learning-based epileptic seizure detection, etc [6-11].

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