Image registration is the process of using a reference image to map the input images to match the corresponding images based on certain features. It has the ability to assist the physicians in the diagnosis and following up on the patient’s condition. One of the main challenges of the registration is that it takes a huge time to be computationally efficient, accurate, and robust as it can be framed as an optimization problem. In this paper, we introduce a comparative study to investigate the influence of the registration step exclusion from the preprocessing pipeline and study the counter

Cardiac magnetic resonance perfusion (CMRP) images are used to assess the local function and permeability of the heart muscle. The perfusion analysis requires the segmentation of cardiac inner and outer walls of the left ventricle (LV). However, the available perfusion datasets are limited or have no annotations. A fair dataset was annotated to employ the latest and most effective Deep Learning (DL) methodologies. In this paper, we employ similar cardiac imaging protocols in terms of cardiac geometry by initially training using CINE images and performing domain adaptation to CMRP images using

Sepsis is a potentially life-threatening medical condition that increases mortality in pediatric populations admitted in the intensive care unit (ICU). Due to the unpredictable nature of the disease course, it was challenging to find the informative genetic biomarkers at the earliest stages. Consequently, a considerable attention has been paid for the early prediction of pediatric sepsis based on genetic biomarkers analysis that would promote the early medical intervention. Therefore, the proposed study attempted to demonstrate the feasibility of Henry Gas Solubility Optimization (HGSO) in

The coronavirus disease 2019 (COVID-19) pandemic has been spreading quickly, threatening the public health system. Consequently, positive COVID-19 cases must be rapidly detected and treated. Automatic detection systems are essential for controlling the COVID-19 pandemic. Molecular techniques and medical imaging scans are among the most effective approaches for detecting COVID-19. Although these approaches are crucial for controlling the COVID-19 pandemic, they have certain limitations. This study proposes an effective hybrid approach based on genomic image processing (GIP) techniques to

Catheters are life support devices. Human expertise is often required for the analysis of X-rays in order to achieve the best positioning without misplacement complications. Many hospitals in underprivileged regions around the world lack the sufficient radiology expertise to frequently process X-rays for patients with catheters and tubes. This deficiency may lead to infections, thrombosis, and bleeding due to misplacement of catheters. In the last 2 decades, deep learning has provided solutions to various problems including medical imaging challenges. So instead of depending solely on

Alzheimer’s Disease (AD) is a dangerous disease that is known for its characteristics of eroding memory and destroying the brain. The classification of Alzheimer's disease is an important topic that has recently been addressed by many studies using Machine Learning (ML) and Deep Learning (DL) methods. Most research papers tackling early diagnosis of AD use these methods as a feature extractor for neuroimaging data. In our research paper, the proposed algorithm is to optimize the performance of the prediction of early diagnosis from the multimodal dataset by a multi-step framework that uses a

Brain Connectivity refers to networks of functional and anatomical connections found throughout the brain. Multiple neural populations are connected by intricate connectivity circuits and interact with one another to exchange information, synchronize their activity, and participate in the accomplishment of complex cognitive tasks. Issues about how various brain regions contribute to cognition and their reciprocal roles have drawn the attention of researchers since the beginning of neuroscience. The interest in brain connection estimation has grown significantly due to the advancement of

For many years, lung cancer has been ranked among the deadliest illnesses in the world. Therefore, it must be anticipated and detected at an early stage. We need to build a computer-aided diagnosis (CAD) system to help physicians to provide better treatment. In this study, the whole pipeline and the process of the CAD system for lung cancer detection in Chest X-ray are provided. It demonstrates the limitations and the problems facing lung cancer detection. New work is highlighted to be explored by the researchers in this area. Existing studies in the field are reviewed, including their

Coronavirus (CoV) disease 2019 (COVID-19) is a severe pandemic affecting millions worldwide. Due to its rapid evolution, researchers have been working on developing diagnostic approaches to suppress its spread. This study presents an effective automated approach based on genomic image processing (GIP) techniques to rapidly detect COVID-19, among other human CoV diseases, with high acceptable accuracy. The GIP technique was applied as follows: first, genomic graphical mapping techniques were used to convert the genome sequences into genomic grayscale images. The frequency chaos game

People with visual impairments often have difficulty locating misplaced objects. This can be a major barrier to their independence and quality of life. Retinal prostheses can restore some vision to people with severe vision loss. We introduce a novel real-time system for locating any misplaced objects for people with visual impairments using retinal prostheses. The system combines One For All (OFA) for Visual Grounding and Google Speech Recognition to identify the object to be located. It then uses an image processing technique called grabCut to extract the object from the background to