In recent years, there have been multiple works of literature reviewing methods for
automatically segmenting multiple sclerosis (MS) lesions. However, there is no literature …

The early detection of breast cancer (BC) has a significant impact on reducing the disease's
mortality rate. As an effective cost-and time-saving tool, computer-aided diagnosis (CAD) …

Manual segmentation of multiple sclerosis (MS) in brain imaging is a challenging task due to
intra and inter-observer variability resulting in poor reproducibility. To overcome the …

Detecting and classifying a brain tumor is a challenge that consumes a radiologist's time and
effort while requiring professional expertise. To resolve this, deep learning techniques can …

Head computed tomography (CT) is the gold standard in emergency departments (EDs) to
evaluate mild traumatic brain injury (mTBI) patients, especially for paediatrics. Data-driven …

In this study, we propose a hybrid approach involving feature extraction, feature selection,
and optimized learning for the diagnosis of multiple sclerosis (MS), which can detect the …

It is essential to detect white matter lesions in 3D Magnetic Resonance Images (MRIs) of
patients with Multiple Sclerosis for diagnosis and treatment evaluation of MS accurately. It is …

Multiple sclerosis can damage the central nervous system, and current drugs are difficult to
completely cure symptoms. The aim of this paper was to use deep learning methods to …

Based on the coherent data provided, artificial intelligence (AI) has widely tied the practice of
medicine at diverse diagnosis levels. Various biosignals can be utilized to examine a …

The rise of deep learning techniques, such as a convolutional neural network (CNN) in
solving medical image problems, offered fascinating results that motivated researchers to …