Irbm 

About: Irbm is an academic journal published by Elsevier BV. The journal publishes majorly in the area(s): Computer science & Medicine. It has an ISSN identifier of 1876-0988. Over the lifetime, 843 publications have been published receiving 9248 citations. The journal is also known as: Ingénierie et recherche biomédicale.

TeleOphta: Machine learning and image processing methods for teleophthalmology

Abstract: A complete prototype for the automatic detection of normal examinations on a teleophthalmology network for diabetic retinopathy screening is presented. The system combines pathological pattern mining methods, with specific lesion detection methods, to extract information from the images. This information, plus patient and other contextual data, is used by a classifier to compute an abnormality risk. Such a system should reduce the burden on readers on teleophthalmology networks.

Deep Transfer Learning Based Classification Model for COVID-19 Disease.

Abstract: The COVID-19 infection is increasing at a rapid rate, with the availability of limited number of testing kits. Therefore, the development of COVID-19 testing kits is still an open area of research. Recently, many studies have shown that chest Computed Tomography (CT) images can be used for COVID-19 testing, as chest CT images show a bilateral change in COVID-19 infected patients. However, the classification of COVID-19 patients from chest CT images is not an easy task as predicting the bilateral change is defined as an ill-posed problem. Therefore, in this paper, a deep transfer learning technique is used to classify COVID-19 infected patients. Additionally, a top-2 smooth loss function with cost-sensitive attributes is also utilized to handle noisy and imbalanced COVID-19 dataset kind of problems. Experimental results reveal that the proposed deep transfer learning-based COVID-19 classification model provides efficient results as compared to the other supervised learning models.

A proposal for the classification and evaluation of fall detectors

Abstract: Falls affect, each year, tens of million of elderly people throughout the world. It can have immediate lethal consequences but also causes many disabling fractures and dramatic psychological consequences which reduce the independence of the person. Falls in the elderly is thus a major public health problem. The “early” detection of fall consequently raises the interest of searchers, as most of elderly fallers cannot return to a standing position on their own following a fall. It is also an interesting scientific problem because it is an ill-defined process. The goals of this study were to classify various approaches used to detect the fall and to point out the difficulty to compare the results of these studies, as there is currently no common evaluation benchmark.

A review of methods for non-invasive and continuous blood pressure monitoring: Pulse transit time method is promising?

Abstract: Most medical examinations include measuring of blood pressure because it is a very good indicator about the status of the cardiovascular system, and it helps medical doctors to adjust the ideal treatment. It would be very useful to know the profile of pressure values during long time period, especially during standard daily activities. It could be used for homecare monitoring and prevention of premature death. Problem is that blood pressure is monitored mainly at discrete time intervals and use an inflatable cuff placed around the arm, which is uncomfortable for the patient. Currently, there are a few technical solutions for continuous non-invasive measurement of blood pressure. This review is focused on the description of methods for continuous blood pressure measurement including their limitations and especially is focused on the description method based on pulse transit time. This method could be the most widely used of methods for non-invasive long time monitoring of blood pressure in the future but now there are still a lot of unsolved problems.

Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays.

Abstract: The most widely used novel coronavirus (COVID-19) detection technique is a real-time polymerase chain reaction (RT-PCR) However, RT-PCR kits are costly and take 6-9 hours to confirm infection in the patient Due to less sensitivity of RT-PCR, it provides high false-negative results To resolve this problem, radiological imaging techniques such as chest X-rays and computed tomography (CT) are used to detect and diagnose COVID-19 In this paper, chest X-rays is preferred over CT scan The reason behind this is that X-rays machines are available in most of the hospitals X-rays machines are cheaper than the CT scan machine Besides this, X-rays has low ionizing radiations than CT scan COVID-19 reveals some radiological signatures that can be easily detected through chest X-rays For this, radiologists are required to analyze these signatures However, it is a time-consuming and error-prone task Hence, there is a need to automate the analysis of chest X-rays The automatic analysis of chest X-rays can be done through deep learning-based approaches, which may accelerate the analysis time These approaches can train the weights of networks on large datasets as well as fine-tuning the weights of pre-trained networks on small datasets However, these approaches applied to chest X-rays are very limited Hence, the main objective of this paper is to develop an automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays by using the extreme version of the Inception (Xception) model Extensive comparative analyses show that the proposed model performs significantly better as compared to the existing models