most citedImproving performance of CNN to predict likelihood of COVID-19 using chest X-ray images with preprocessing algorithms

432 citations · 434 across the 3 of their papers we have counts for

collaborators

7 papers

eess.IV2020

A practical method for pupil segmentation in challenging conditions

Donya Khaledyan, Mohammad Eshghi, Morteza Heidari +2

Various methods have been proposed for authentication, including password or pattern drawing, which is clearly visible on personal electronic devices. However, these methods of aut…

eess.IV2020

Image quality enhancement in wireless capsule endoscopy with adaptive fraction gamma transformation and unsharp masking filter

Rezvan Ezatian, Donya Khaledyan, Kian Jafari +3

Wireless Capsule Endoscopy (WCE) presented in 2001 as one of the key approaches to observe the entire gastrointestinal (GI) tract, generally the small bowels. It has been used to d…

eess.IV2020

Deep learning denoising for EOG artifacts removal from EEG signals

Najmeh Mashhadi, Abolfazl Zargari Khuzani, Morteza Heidari +1

There are many sources of interference encountered in the electroencephalogram (EEG) recordings, specifically ocular, muscular, and cardiac artifacts. Rejection of EEG artifacts is…

cs.CV2020

An approach to human iris recognition using quantitative analysis of image features and machine learning

Abolfazl Zargari Khuzani, Najmeh Mashhadi, Morteza Heidari +1

The Iris pattern is a unique biological feature for each individual, making it a valuable and powerful tool for human identification. In this paper, an efficient framework for iris…

cs.CV2020

Applying a random projection algorithm to optimize machine learning model for breast lesion classification

Morteza Heidari, Sivaramakrishnan Lakshmivarahan, Seyedehnafiseh Mirniaharikandehei +4

Machine learning is widely used in developing computer-aided diagnosis (CAD) schemes of medical images. However, CAD usually computes large number of image features from the target…

cs.LG20202 cited

Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images

Seyedehnafiseh Mirniaharikandehei, Morteza Heidari, Gopichandh Danala +2

Background and Objective: Non-invasively predicting the risk of cancer metastasis before surgery plays an essential role in determining optimal treatment methods for cancer patient…