Medical data processing has grown into a prominent topic in the latest decades with the
primary goal of maintaining patient data via new information technologies, including the …

The scarcity of high-quality annotated medical imaging datasets is a major problem that
collides with machine learning applications in the field of medical imaging analysis and …

Automated retinal image analysis holds prime significance in the accurate diagnosis of
various critical eye diseases that include diabetic retinopathy (DR), age-related macular …

Data-driven Machine and Deep Learning (ML/DL) is an emerging approach that uses
medical data to build robust and accurate ML/DL models that can improve clinical decisions …

Retinal optical coherence tomography (OCT) images provide fundamental information
regarding the health of the posterior eye (eg, the retina and choroid). Thus, the development …

Glaucoma is a leading cause of irreversible vision impairment globally, and cases are
continuously rising worldwide. Early detection is crucial, allowing timely intervention that can …

Medical image interpretation is an essential task for the correct diagnosis of many diseases.
Pathologists, radiologists, physicians, and researchers rely heavily on medical images to …

AI is a broad concept, grouping initiatives that use a computer to perform tasks that would
usually require a human to complete. AI methods are well suited to predict clinical outcomes …

Diabetic retinopathy (DR), a chronic disease in which the retina is damaged due to small
vessel damage caused by diabetes mellitus, is one of the leading causes of vision …

High-dimensional medical data makes prediction a complex and difficult task. This study
aims at modeling predictive models for medical data. Two datasets of medical data are …