Dr. Sahar Selim

Circulatory failure, also known as shock, is a critical condition that can have serious consequences for one's health. Early detection and timely intervention are crucial for improving patient outcomes. Machine learning (ML) models have shown promise in predicting circulatory failure based on clinical data. In our study, we examined different machine learning (ML) models to predict circulatory

A brain tumor is an extremely hazardous illness that can affect people of any age. Less than 50% of individuals with brain cancer have a chance of surviving. As a result, precise segmentation of brain tumors is crucial for the diagnosis, planning of the course of treatment, and tracking of the tumor growth. Deep Learning (DL) models can increase the precision and speed of brain tumor diagnosis by

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

Nuclei detection and segmentation in histopathological images is a prerequisite step for quantitative analysis including morphological shape and size to help in identifying cancer prognosis. Digital pathology field aims to improve the quality of cancer diagnosis and has helped pathologists to reduce their efforts and time. Different deep learning architectures are widely used recently in Digital

With the extensive usage of mobile applications in daily life, it has become crucial for the companies of software to develop applications for the most popular platforms such as Android and iOS in the shortest possible time and at the lowest possible cost. However, ensuring consistent UIs and functionalities among cross-platform versions can be challenging and costly since different platforms have

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

X-ray has a huge popularity around the world. This is due to its low cost and easy to access. Most of lung diseases are diagnosed using Chest X-ray (CXR). So, developing computer aided detection (CAD) provided with automatic lung segmentation can improve the efficiency of the detection and support the physicians to make a reliable decision at early stages. But when the input image has image

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

Alzheimer's disease (AD) is considered the 6 th leading cause of death worldwide. Early diagnosis of AD is not an easy task, and no preventive cures have been discovered yet. Having an accurate computer-aided system for the early detection of AD is important to help patients with AD. This study proposes a new approach for classifying disease stages. First, we worked on the MRI images and split