This paper tries to give a gentle introduction to deep learning in medical image processing,
proceeding from theoretical foundations to applications. We first discuss general reasons for …

The advent of computed tomography (CT) has revolutionized radiology, and this revolution
is still going on. Starting as a pure head scanner, modern CT systems are now able to …

Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …

Colorectal cancer has a great incidence rate worldwide, but its early detection significantly
increases the survival rate. Colonoscopy is the gold standard procedure for diagnosis and …

Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss,
predominantly affecting the working-age population across the globe. Screening for DR …

Medical image reconstruction aims to acquire high-quality medical images for clinical usage
at minimal cost and risk to the patients. Deep learning and its applications in medical …

Deep learning (DL) is one of the branches of artificial intelligence that has seen exponential
growth in recent years. The scientific community has focused its attention on DL due to its …

Image reconstruction in magnetic resonance imaging (MRI) and computed tomography (CT)
is a mathematical process that generates images at many different angles around the …

In modern‐day medicine, medical imaging has undergone immense advancements and can
capture several biomedical images from patients. In the wake of this, to assist medical …

Colorectal Cancer (CRC), a leading cause of cancer-related deaths, can be abated by timely
polypectomy. Computer-aided classification of polyps helps endoscopists to resect timely …