A Variational Network for Biomedical Images Denoising using Bayesian model and Auto-Encoder.

Auto-encoders have demonstrated outstanding performance in computer vision tasks such as biomedical imaging, including classification, segmentation, and denoising. Many of the current techniques for image denoising in biomedical applications involve training an autoencoder or convolutional neural network (CNN) using pairs of clean and noisy images. However, these approaches are not realistic because the autoencoder or CNN is trained on known noise and does not generalize well to new noisy distributions.

An improved semi-supervised segmentation of the retinal vasculature using curvelet-based contrast adjustment and generalized linear model.

Diagnosis of most ophthalmic conditions, such as diabetic retinopathy, generally relies on an effective analysis of retinal blood vessels. Techniques that depend solely on the visual observation of clinicians can be tedious and prone to numerous errors. In this article, we propose a semi-supervised automated approach for segmenting blood vessels in retinal color images.

An accurate unsupervised extraction of retinal vasculature using curvelet transform and classical morphological operators.

Background: Many ophthalmic disorders such as diabetic retinopathy and hypertension can be early diagnosed by analyzing changes related to the vascular structure of the retina. Accuracy and efficiency of the segmentation of retinal blood vessels are important parameters that can help the ophthalmologist to better characterize the targeted anomalies.

Method: In this work, we propose a new method for accurate unsupervised automatic segmentation of retinal blood vessels based on a simple and adequate combination of classical filters.

An Overview of Supervised Machine Learning Methods and Data Analysis for COVID-19 Detection.

Methods: Our analysis and machine learning algorithm is based on most cited two clinical datasets from the literature: one from San Raffaele Hospital Milan Italia and the other from Hospital Israelita Albert Einstein São Paulo Brasilia. The datasets were processed to select the best features that most influence the target, and it turned out that almost all of them are blood parameters. EDA (Exploratory Data Analysis) methods were applied to the datasets, and a comparative study of supervised machine learning models was done, after which the support vector machine (SVM) was selected as the one with the best performance.

Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms.

In modern-day medicine, medical imaging has undergone immense advancements and can capture several biomedical images from patients. In the wake of this, to assist medical specialists, these images can be used and trained in an intelligent system in order to aid the determination of the different diseases that can be identified from analyzing these images. Classification plays an important role in this regard; it enhances the grouping of these images into categories of diseases and optimizes the next step of a computer-aided diagnosis system.

Simulation of an optimized technique based on DS-CDMA for simultaneous transmission of multichannel biosignals.

Telemedicine is becoming increasingly, with applications in many areas of healthcare, such as home telecare of the elderly, diagnosis at a distance and robotic surgery. The simultaneous transmission of several leads of biomedical signals should be considered in telemedicine, given the many benefits it brings. Code division multiple access (CDMA) is a multiple access technique that enables users to transmit independent information simultaneously within the same bandwidth.

ECG data compression using Jacobi polynomials.

Data compression is a frequent signal processing operation applied to ECG. We present here a method of ECG data compression utilizing Jacobi polynomials. ECG signals are first divided into blocks that match with cardiac cycles before being decomposed in Jacobi polynomials bases.