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

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EDITORIAL

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

Rahul K. Kher1 , * Open Modal Chirag Paunwala2 Falgun Thakkar1 Heena Kher3 Mita Paunwala4
Authors Info & Affiliations
The Open Biomedical Engineering Journal • 31 Dec 2021 • EDITORIAL • DOI: 10.2174/1874120702115010131

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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