Recent Developments in Detection of Central Serous Retinopathy through Imaging and Artificial Intelligence Techniques A Review

The Central Serous Retinopathy (CSR) is a major significant disease responsible for causing blindness and vision loss among numerous people across the globe. This disease is also known as the Central Serous Chorioretinopathy (CSC) occurs due to the accumulation of watery fluids behind the retina. The detection of CSR at an early stage allows taking preventive measures to avert any impairment to the human eye. Traditionally, several manual detection methods were developed for observing CSR, but they were proven to be inaccurate, unreliable, and time-consuming. Consequently, the research community embarked on seeking automated solutions for CSR detection. With the advent of modern technology in the 21st century, Artificial Intelligence (AI) techniques are immensely popular in numerous research fields including the automated CSR detection. This paper offers a comprehensive review of various advanced technologies and researches, contributing to the automated CSR detection in this scenario. Additionally, it discusses the benefits and limitations of many classical imaging methods ranging from Optical Coherence Tomography (OCT) and the Fundus imaging, to more recent approaches like AI based Machine/Deep Learning techniques. Study primary objective is to analyze and compare many Artificial Intelligence (AI) algorithms that have efficiently achieved automated CSR detection using OCT imaging. Furthermore, it describes various retinal datasets and strategies proposed for CSR assessment and accuracy. Finally, it is concluded that the most recent Deep Learning (DL) classifiers are performing accurate, fast, and reliable detection of CSR.

Cursive Multilingual Characters Recognition Based on Hard Geometric Features

The cursive nature of multilingual characters segmentation and recognition of Arabic, Persian, Urdu languages have attracted researchers from academia and industry. However, despite several decades of research, still multilingual characters classification accuracy is not up to the mark. This paper presents an automated approach for multilingual characters segmentation and recognition. The proposed methodology explores character based on their geometric features. However, due to uncertainty and without dictionary support few characters are over-divided. To expand the productivity of the proposed methodology a BPN is prepared with countless division focuses for cursive multilingual characters. Prepared BPN separates off base portioned indicates effectively with rapid upgrade character acknowledgment precision. For reasonable examination, only benchmark dataset is utilized.