Convolutional Neural Networks

Elsevier, Measurement: Journal of the International Measurement Confederation, Volume 171, February 2021
In the recent past, biomedical domain has become popular due to digital image processing of accurate and efficient diagnosis of clinical patients using Computer-Aided Diagnosis (CAD). Appropriate and punctual disease identification and treatment arrangement directs to enhance superiority of life and improved life hope in Alzheimer Disease (AD) patients. The cutting-edge approaches that believe multimodal analysis have been shown to be efficient and accurate are improved compared with manual analysis.
Background: Hippocampus segmentation on magnetic resonance imaging is of key importance for the diagnosis, treatment decision and investigation of neuropsychiatric disorders. Automatic segmentation is an active research field, with many recent models using deep learning. Most current state-of-the art hippocampus segmentation methods train their methods on healthy or Alzheimer's disease patients from public datasets. This raises the question whether these methods are capable of recognizing the hippocampus on a different domain, that of epilepsy patients with hippocampus resection.
Diabetic Retinopathy (DR) is one of the leading causes of preventable blindness in the working-age diabetic population in India and across the world. It may lead to permanent blindness if not detected in the early stages. The prevalence of DR among diabetics in India was 10% and 16.9% in 2014 and 2019, respectively. In 2019, the International Diabetes Federation estimated that Diabetic Mellitus will affect 101 million people in India in 2030; the largest number in any nation in the world.