As part of machine learning, you can recognize images, detect and track objects, analyze documents automatically, recognize faces, and perform computational photography, augmented reality, and medical image processing, among other tasks. In recent years, advances in advanced computing and imaging systems in biomedical engineering have given rise to a new research dimension, and the size of biomedical data continues to grow. As a result of the use of deep learning algorithms in biomedical devices, features can be learned automatically, allowing for the analysis of signals and the identification of diseases. In biomedical engineering, Machine can be applied to four different fields. They include bio and medical images analysis, brain, body, and machine interfaces, genomic sequencing, and gene expression analysis, as well as public and medical health management systems.

The goal of this special issue is to foster a community of authors and readers to discuss and develop new ideas and research directions in the field of Machine Learning for biomedical engineering. Articles are peer-reviewed by academics or field experts. The main criteria for publication are that the articles describe actions that are useful, original, significant and reproducible. Additionally, editorials and commentaries are published by experts in a variety of fields, encouraging authors to collaborate and share critical analysis.

Suitable topics of interest include, but are not limited to:

(1) Machine Learning in Deep Brain Stimulation

(2) Machine Learning Algorithms in Statistical Analysis of Big Biomedical data

(3) Machine Learning in BIOMEMS Analysis

(4) Machine Learning in Biosensorsn

(5) Machine Learning in detection of COVID-19 Fake News on Social Media Platforms

(6) Machine Learning in the analysis of COVID-19 related datan

(7) Machine Learning in Medical image processing

(8) Machine Learning in cancer analysis

(9) Machine Learning in MRI